Protein Folding, Prions, and Disease

Part 1: Protein Folding in Infectious Disease and Cancer

00:08.0 Hi there. 00:09.1 I'm Susan Lindquist 00:10.2 and I'm a member of the Howard Hughes Medical Institute 00:12.1 and I work at MIT 00:14.1 at the Whitehead Institute. 00:15.2 I'm here to tell you about 00:17.2 a variety of different problems in protein folding. 00:20.0 In this particular lecture, I'm going to talk to you about 00:22.1 protein folding and how it manifests 00:24.1 in a wide variety of human diseases. 00:27.0 So, let me first set the problem for you. 00:29.3 Proteins come out of the ribosome as 00:32.1 long, linear strings of amino acids 00:34.1 and they fold up into these 00:36.1 incredibly complicated shapes 00:37.3 in order to do their jobs, 00:39.1 and they do just about everything inside of living systems. 00:41.3 And the problem, the difficulty in getting those folds exactly right, 00:45.3 and they have to get them exactly right 00:48.1 or the protein either won't function, which is bad, 00:50.1 or it will go off and do renegade, bad things, 00:53.3 interacting with the wrong kinds of proteins, 00:55.2 and that's even worse. 00:57.0 The problem with them reaching their final functional states 01:00.1 with these complicated folds 01:02.1 is that they have to do it in the 01:04.2 insanely crowded environment of a living cell. 01:07.1 So, this wonderful movie by Adrian Elcock 01:10.2 gives you an example of how crowded the cell is 01:13.2 and how jumbled the proteins are 01:16.2 and how they're banging into each other all the time. 01:18.3 And this crowding and chaos and energy is really part of life, 01:22.0 what makes life possible, 01:23.3 but it also means that all living systems 01:26.2 are kind of on a knife edge of vulnerability, 01:28.3 because it's incredibly kinetic in there, 01:33.1 in fact that movie, 01:36.0 in order to be really accurate, 01:37.2 would have to be sped up by 3-million fold. 01:41.1 So you can imagine that if there are any perturbations in the system, 01:45.2 proteins will wind up starting to bump into each other 01:48.1 in inappropriate ways 01:49.2 and start sticking to each other 01:51.1 and making a mess. 01:52.3 And that happens with all sorts of different stresses, 01:55.2 it can influence the process of evolution, 01:58.0 and it very strongly influences every aspect of human disease. 02:04.2 So, you have a very visceral feeling for what is protein aggregation 02:10.0 in your own lives. 02:12.2 This is it. 02:14.0 These are proteins and they look perfectly fine. 02:16.1 This is what happens when you apply heat to the system -- 02:19.3 the proteins aggregate. 02:22.1 And of course that doesn't happen in your body, 02:25.0 but just a little bit of that is actually happening all the time, 02:28.2 and if it doesn't get corrected 02:31.1 it can spell absolute disaster for living systems. 02:34.2 And so cells use a tremendous amount of energy 02:37.2 and lots of different proteins 02:39.1 to actually try to keep the state of their proteins like this 02:43.1 -- clear, beautiful, fluid -- 02:46.0 and to prevent that kind of protein aggregation. 02:50.0 And I first got into this problem 02:53.0 when I was working on something called the heat shock response. 02:55.1 The heat shock response is universal 02:57.1 just as the protein folding problem is universal, 03:00.1 and it works like this. 03:03.2 You can see in yeast, on the far side there, 03:07.0 we've taken two aliquots of a culture of yeast 03:09.2 that are growing perfectly happily 03:11.1 at normal temperatures 03:13.0 and we expose them both to high temperatures. 03:15.2 But for one of them, 03:17.2 we first gave the cells a mild pretreatment 03:20.1 at 39Ã‚Â°C for just half an hour, 03:23.2 and you can see that the difference that the pretreatment made 03:26.1 in their ability to survive. 03:28.2 The middle there is a similar experiment done with Arabidopsis seedlings, 03:32.0 and this experiment here is one done with tumor cells, 03:35.3 and it's been done with every organism that you can imagine. 03:38.3 It's universal. 03:41.1 Pretreatments allow protection 03:45.1 for much more severe stresses. 03:46.3 So, what are the cells doing, 03:49.1 what's happening in those biological systems, 03:51.2 to get them prepared for these 03:54.2 difficult, stressful conditions? 03:57.1 They're making heat shock proteins. 03:59.1 So, this is a labeling of the proteins that are made 04:05.1 by cells at either 25Ã‚Â°C or 39Ã‚Â°C, 04:08.0 and the different bands on that gel are just spread out 04:11.0 according to the molecular weight of the proteins, 04:12.3 and you can see that there are massively new proteins 04:17.3 that are made in just tremendously large amounts, 04:20.0 they just stick right out at you in the gel. 04:22.0 They're called HSPs for Heat Shock Proteins, 04:24.2 because they were first found in response to heat, 04:27.1 but we now know they actually exist 04:30.1 and they are induced 04:32.1 and made at higher levels 04:34.0 in response to just about every stress that you can think of 04:36.1 -- so, higher temperatures, 04:38.1 osmotic stress, 04:39.2 changes in pH, 04:40.2 changes in energy balance in the cell -- 04:44.3 and they fix those problems with protein folding. 04:47.2 They either prevent the proteins from misfolding in the first place 04:52.2 or they take proteins that have been damaged 04:54.2 and have aggregated and start to get into a coddled egg sort of state, 04:57.3 and get them right back out of it. 05:01.1 Every organism on the planet 05:03.3 uses this same response. 05:05.1 This gel that I'm showing you here 05:07.2 happens to be a gel of yeast proteins 05:10.0 and the yeast heat shock response. 05:12.1 Bacteria, plants mammals -- 05:14.3 we all do it. 05:16.3 Birds do it, bees do it, even educated fleas do it. 05:20.2 So it's very universal 05:22.2 and it's a very broadly used survival response. 05:25.0 It's used not only in response, as I mentioned, to heat... 05:27.1 it protects organisms not just from heat... 05:29.2 it protects organisms from all of those other stresses 05:32.1 I told you about. 05:35.0 And this manifests in many different ways in biology 05:36.3 and, as I mentioned, 05:39.2 every different aspect of human disease 05:42.0 that you can imagine. 05:43.2 And we actually work, 05:45.1 because we started out working on protein folding 05:46.2 a long time ago, 05:48.1 and it had this ramification in different ways, 05:50.1 we actually found ourselves working 05:52.0 in all these different areas of biology. 05:53.3 I'm only going to tell you one little bit 05:56.0 about how this protein folding problem 05:59.1 interfaces with infectious diseases, 06:01.2 then I'll tell you a little bit more about what we're doing with 06:04.1 cancer biology and neurodegenerative diseases. 06:07.1 So, this is an example of an organism, 06:11.1 a pathological organism, 06:13.2 invading us through the bite of a mosquito. 06:15.2 This is a malaria parasite. 06:17.1 And you can see that when the mosquito first bites, 06:20.1 of course the organism undergoes a big change of temperature 06:24.0 when it comes inside of us, 06:25.2 and so that causes protein folding stress, 06:27.2 it mounts a heat shock response to help protect it, 06:30.2 and then in the life cycle this organism is moving around 06:34.1 and going to different parts of the body, 06:36.0 and as it does that, our bodies mount, 06:39.1 recognize that they're having problems with protein folding, 06:41.3 because of the heat stress, 06:43.2 and our bodies try to make it even worse for the organism 06:46.2 by mounting massive fevers. 06:49.1 And so this is a constant feature 06:51.2 of all pathological organisms that invade us 06:54.0 and change from one temperature to another. 06:56.1 We actually are only working a little bit on malaria, 06:59.1 but we have a major project going on 07:01.2 where we're trying to block the ability of 07:05.2 fungal organisms, fungal pathogens, 07:08.1 to use this heat shock response 07:09.3 to protect themselves when they get inside of us. 07:13.1 Unfortunately, I won't have time to talk to you about that today, 07:15.2 but I do want to talk to you about 07:17.2 aspects of protein folding in cancer 07:19.2 and neurodegenerative diseases. 07:21.2 So, with respect to cancer and neurodegenerative diseases, 07:24.1 we are actually between a rock and a hard place, 07:29.0 because it turns out that cancer cells 07:31.3 -- and, in fact, those infectious organisms, as I just mentioned -- 07:35.0 are using their survival response to help kill us, 07:38.2 empower them to get through all kinds of stresses 07:41.2 and power on through those stresses and kill us. 07:45.2 And oddly enough, even though our brains 07:48.0 during these neurodegenerative diseases 07:49.3 are also experiencing misfolded proteins, 07:53.0 the massive aggregates of proteins 07:55.3 that I'll show you in a minute, 07:57.1 in fact, that occur in our brains with these diseases, 07:59.3 those usually should signal, 08:01.2 "Uh-oh... our proteins are in trouble, 00:08:03.07 let's mount a heat shock response," 08:05.0 and for some reason we don't quite understand, 08:07.0 they don't do that or they at least don't do it very well. 08:11.2 So, we're kind of in a mess right there. 08:14.2 In that balance of cancer and neurodegeneration, 08:19.2 one of the major players, 08:21.2 and there are more and more of them that are coming out now 08:24.0 but one of the major players that we've found 08:26.2 that regulates that balance between health and disease -- 08:29.1 two different diseases at these different ends, 08:31.1 health being in the middle 08:33.3 -- is something called the heat shock factor, 08:36.1 it's the master regulator of that heat shock response that I just showed you, 08:40.2 it's the protein that goes into the nucleus, 08:42.2 binds to those genes, turns them all on, 08:44.1 and gets the response going. 08:46.1 And so it's a pretty powerful mediator 08:50.0 of this balance between health and disease. 08:53.3 So, our first understanding of how this heat shock response, 08:57.1 this classic heat shock survival response, 09:00.2 plays a role in cancer 09:03.1 was when we were able to obtain a mouse 09:06.1 that actually had a mutation in that heat shock factor. 09:09.1 It couldn't mount a heat shock response. 09:11.0 And that mouse was actually 09:13.2 more susceptible to heat stress 09:15.2 and other stresses like that, 09:18.2 but it was perfectly fine in terms of living a normal lifetime, 09:20.1 and as long as it was in a relatively controlled environment, 09:22.2 it was just fine. 09:24.1 And Ivor Benjamin, who'd made this mouse, 09:26.2 was kind enough to share it with us. 09:29.1 So, we looked at how being 09:33.1 unable to mount a heat shock response like that 09:35.2 might affect the susceptibility to cancer. 09:39.1 And it had a pretty big effect. 09:41.0 So, this is the first major experiment that we did. 09:43.2 What we did was we shaved the backs of these mice 09:46.1 and we painted them with a mutagen 09:48.3 and a tumor-promoting agent, 09:51.2 and you can see that the normal mouse is responding 09:54.1 to this very classic cancer-causing agent 09:58.0 by producing skin tumors. 10:02.1 And these skin tumors, 10:05.2 you're seeing them at a midway stage of the disease, 10:07.3 but they got worse and worse and worse, 10:09.1 and they eventually killed the mouse. 10:11.2 The mice down here are genetically identical 10:14.0 to these mice up here 10:16.2 #NAME? 10:19.1 has been mutated in the genome 10:21.2 and it can't mount the heat shock response. 10:24.1 And you can see that that profoundly protects the mouse from cancer, 10:28.0 because the cancer cells 10:30.1 are actually using that survival response 10:32.2 to allow them to grow in an uncontrolled way 10:35.2 and to invade other systems and other places in the body 10:38.0 where they normally don't belong 10:39.2 and where they encounter stressful environments. 10:43.2 So, that's how it played out in the mouse 10:46.1 and it turned out that we studied several different causes of cancer 10:50.3 #NAME? 10:55.3 that's kind of a classic pathway in cancer biology, 10:58.2 but there are other equally important pathways in cancer biology 11:01.2 and we looked at several of them. 11:03.3 And in each case we looked at, 11:05.3 there was a big difference in the incidence of cancer. 11:08.1 Not being able to mount the heat shock response 11:10.2 meant those tumors didn't develop as much 11:13.0 and they were not as invasive. 11:16.0 In fact, this difference in the number of tumors 11:20.0 translated into an incredible difference in lifespan. 11:23.1 These mice up here died, 11:25.2 these mice did not, 11:27.3 and that happened again and again with these cancer models, 11:29.3 and it's been shown by other laboratories, now, as well. 11:33.2 So, that's mice. What about humans? 11:36.2 Well, one of the things that we did with human 11:39.2 to try to see if this was really relevant to human beings, 11:42.1 was to take a variety of different cancer cell lines from humans, 11:46.0 lines that could be grown in culture and manipulated easily, 11:48.3 and we knocked out their ability to make a heat shock response, 11:51.2 and in fact they started to die. 11:53.3 But if we did that with a normal cell 11:56.3 and kept under fairly normal conditions, they were fine. 11:58.2 But when we did that with cells that were cancerous, 12:01.0 from all sorts of different cancers, 12:02.3 from all sorts of different causes, 12:04.2 they died. 12:06.1 But you've got a problem here now. 12:08.0 We're looking at mice and we're looking at tissue culture cells in a dish, 12:11.2 a very abnormal environment... 12:14.0 can we get any idea, can we get any evidence 12:16.2 that this really matters in a human being? 12:18.2 And so what we did, 12:21.0 one of the pathologists working in my group 12:23.1 and another pathologist working in Bob Weinberg's group, 12:26.3 Tan Ince, and Sandro Santagata in my group, 12:30.2 looked at whether or not, 12:34.1 when you took out tumor samples directly from a patient, 12:36.2 and stained them with an antibody 12:41.2 that would react with the heat shock transcription factor 12:43.2 that I just told you about, 12:45.1 would there be a manifestation of the activation o 12:48.0 f that transcription factor 12:49.2 in the human tumors taken 12:52.0 directly and immediately from the body? 12:53.2 And so here you have a slice of human tissue 12:57.1 taken from a breast cancer victim, 13:00.1 and you can see the brown staining is HSF. 13:05.1 The blue is just a generalized protein stain. 13:08.2 And you can see we've caught the border between the normal cells, 13:12.3 here, 13:14.1 and the tumor, 13:15.3 and that's a very special place to be 13:17.1 because we don't have to worry about the 13:19.1 difference in staining being due to a difference in the fixation of the tissue 13:22.2 or whether the antibody was able to penetrate the tissue. 13:26.0 We know that the normal tissue and the tumor tissue, 13:29.1 which were right next to each other, 13:31.0 are being treated in exactly the same way. 13:32.1 And so here we see that HSF, 13:34.3 the brown staining, 13:36.1 is in the cytoplasm, 13:38.2 and that's because that's a normal cell, 13:40.2 they're perfectly happy 13:43.2 and they don't need their heat shock response. 13:45.1 But in the tumor, 13:47.0 you can see that the sort of deranged looking cells 13:50.1 have much higher levels of that brown stain, 13:54.1 much higher levels of the heat shock factor, 13:56.1 and it's moved from the cytoplasm into the nucleus, 13:59.0 where it does its business in transcribing 14:01.2 a whole variety of survival genes. 14:05.0 So, that's breast cancer. 14:07.0 That's pancreatic cancer. 14:09.2 That's colon cancer. 14:13.0 And that's lung cancer. 14:15.1 In fact, we've stained 14:17.2 dozens and dozens of different types of cancers 14:20.2 and we've seen it in every single one of them 14:22.3 that we've looked at 14:24.2 and, moreover, what's interesting about it 14:26.2 and a little bit unusual when it comes to looking at cancer markers, 14:29.0 is that along the tumor, throughout the tumor, 14:32.1 the staining is quite uniform and even 14:35.1 as though the whole tumor is really using that response. 14:39.2 So, that looks interesting, 14:42.1 that this response is being turned on in the cancer cells, 14:46.0 but not in the normal cells that are right next door, 14:48.3 but does it really make a difference? 14:50.2 Well, of course our experiments with mice 14:52.2 and our experiments with tissue culture cells 14:54.1 would suggest that it is making a difference, 14:56.2 but can we get any evidence that it really is? 15:02.1 We can, actually, 15:04.3 we can because of a wonderful group of studies 15:08.2 that have been done over the course of many, many years 15:11.0 by dedicated clinicians. 15:13.3 And the first study that we looked at 15:16.1 was something called the Nurses' Health Study. 15:17.3 Nurses are a particularly wonderful group of patients 15:20.2 because they're very responsible. 15:22.1 When they enroll in a study, 15:24.0 they will come back year after year after year for their examinations, 15:27.3 and so you can track them and find out what happens to them. 15:30.1 So this is a particularly valuable study 15:33.0 and the nurses that enrolled in this study 15:37.0 have been examined for many different pathological conditions 15:41.0 and that study has told us a great deal. 15:43.1 In this particular case, 15:45.1 what we did was to get ahold of 15:49.0 samples from the nurses that were first enrolled in this study 15:52.2 25 years ago, 15:54.3 and we stained their initial samples 15:57.2 on their first biopsy. 16:00.2 We stained them for whether or not they had a high level of HSF 16:03.2 or a low level of HSF, 16:05.1 and whether it was in the nucleus or whether it wasn't. 16:08.1 And then you can see that... 16:10.2 the pathologists stained these slides blind 16:13.2 -- they had no idea what had happened to those nurses -- 16:15.2 and they scored them for high, medium, or low levels. 16:19.1 And then we decoded the data, 16:21.2 because the survival records had been kept for those women, 16:23.3 25 years later, 16:25.3 and sure enough, 16:28.1 in the women who had a low level of the heat shock response 16:33.1 active in their tumors, 16:35.1 they were much less likely to die 16:37.1 than the women who had a high level of HSF in their tumors. 16:41.0 So, this is kind of an amazing difference in these populations 16:45.0 when you realize that these women 16:48.2 were treated in different ways all over the country, 16:51.1 in different clinical settings and clinical centers. 16:55.0 So to see such a large difference in their survival 16:58.1 really would suggest strongly 17:00.1 that the heat shock response, 17:02.0 when it had been on in their tumor to begin with, 17:04.0 it helped those cancer cells to survive 17:08.1 and helped to drive the malignant state 17:11.0 in a very unfortunate way for them. 17:13.1 Now, was this just a one-off from breast cancer? 17:17.2 No, we were able to actually do something similar 17:20.2 in lung and colon cancer, 17:24.1 and these are the three major causes of cancer in the developed world, 17:27.3 the three major killers, 17:30.1 and we think of course it's probably not just these diseases, 17:36.1 not just these cancers, 17:38.1 we think it's much, much broader. 17:39.3 But certainly in these, 17:42.1 we're able to at least get a correlation 17:44.2 between the activity of this heat shock response 17:47.1 and the outcome in terms of malignancy. 17:51.2 And that allows us to ask: 17:54.2 can we do something about it? 17:56.2 And that's what we're trying to do. 17:58.1 We're wondering if we can use it diagnostically. 18:00.2 In other words, if, in fact, 18:03.1 when someone is first diagnosed 18:07.1 as having a cancer in the clinic, 18:09.1 might staining for the heat shock response 18:11.2 help the clinicians to decide 18:13.2 whether or not that patient should be treated 18:15.2 aggressively or not aggressively. 18:17.2 It's often a very, very difficult decision to make. 18:21.3 And we think that you might not want to treat patients 18:25.3 with an aggressive chemotherapy agent 18:27.2 that might wind up inducing a heat shock response, 18:30.2 inducing this survival response, 18:32.1 if the tumor in the first place doesn't have it. 18:35.1 We don't know if that's going to work yet or not, 18:37.1 but that's the logic of what we're trying to do. 18:40.1 And then, okay, that's diagnostics, maybe... 18:44.2 we think it's promising at least, 18:46.2 but then could we ever learn to control it in human cancers? 18:49.2 Could we surgically block, 18:52.2 with chemical agents or with antibodies, 18:55.1 could we surgically block the activation of that response 18:57.2 in tumors where it's already active, 19:00.1 and try to turn it off? 19:02.3 So, that's another thing that we're trying to do. 19:05.0 So, how can we use that combined knowledge therapeutically? 19:09.2 It would be manifested in lots of different ways, 19:12.1 but one thing you can imagine 19:14.1 is you find out whether or not the patients 19:16.1 have that heat shock response, 19:18.1 and you really only treat the patients 19:20.1 that do have the heat shock response on 19:23.1 with that particular agent, 19:24.2 because you don't want to treat people unnecessarily. 19:28.0 So those are the ways in which we hope 19:30.2 to use that response 19:32.2 and use our knowledge of that response. 19:34.2 It's still very early days, 19:36.1 but that's how basic science really tries to 19:39.0 interface with difficult clinical problems. 19:43.1 So, you might think that this powerful, powerful 19:47.2 survival response that the cancer cells are taking advantage, 19:51.0 that our nerve cells are not taking advantage of, 19:53.2 maybe if we turned on that survival response 19:56.1 it might be a great therapeutic strategy 19:58.1 for all of those different protein folding diseases. 20:00.0 It could maybe work on all of them. 20:01.2 I don't think so, however, 20:03.2 and the reason is because 20:07.0 I think turning up that response 20:09.2 when it's not normally needed 20:11.2 might make the brain 20:14.1 more susceptible to development of cancer. 20:16.1 So, in terms of treating neurodegenerative diseases 20:18.2 and trying to find some solution to these horrible diseases, 20:21.2 we're going about that protein folding problem 20:23.3 a little differently. 20:25.2 We're actually trying to attack the individual pathologies 20:30.1 that are caused by individual proteins 20:32.2 that misfold in those diseases 20:34.0 so we can have a much more targeted, 20:36.1 sort of surgical strike 20:38.2 against those diseases, 20:40.0 and I'll be talking about that in my next lecture. 20:43.1 Meanwhile, I just want to thank you for listening.

Part 2: Protein Folding in Neurodegenerative Disease

00:07.0 Hi. I'm Susan Lindquist. 00:09.2 I'm a member of the Howard Hughes Medical Institute 00:11.2 and I work at the Whitehead Institute at MIT. 00:14.3 I work on a variety of different protein folding problems 00:18.0 and in my last lecture 00:20.2 I gave you a broad introduction to the problem, 00:23.1 told you how it manifested, at least a little bit, 00:25.2 how it manifested in infectious diseases, 00:28.1 and more broadly how it is used by cancers 00:31.2 to drive the malignant state. 00:34.0 In this lecture, I'd like to tell you about 00:36.3 a different aspect of protein pathology, 00:38.1 another equally devastating aspect of protein folding pathology 00:42.2 -- the neurodegenerative diseases -- 00:44.1 because all of these diseases are diseases of protein misfolding. 00:48.0 This is an extremely vivid demonstration 00:51.2 of the difference between the brain of a normal adult upon autopsy 00:56.2 versus an adult who died of Alzheimer's disease. 01:01.2 It's obviously a devastating disease 01:04.0 and this is why the people who these diseases 01:06.2 lose their memory, they lose control of functions. 01:09.2 And these diseases are really terrible. 01:16.1 Now, this is a graph 01:19.1 of what's happened to human longevity 01:22.2 over the last couple of hundred years, 01:25.2 and this is really, I think -- 01:28.0 the red and the black are just two different calculations... 01:30.2 it's not so easy going back to the older days 01:33.1 to calculate exactly how old people lived on average, 01:36.2 but these two different ways of doing it 01:40.0 came out with the same answer -- 01:41.2 and you can see that there's been this steady march of progress 01:44.1 and it's just been amazing. 01:45.2 This has been, I think, one of the glories of mankind, 01:48.0 to be able to do this 01:50.0 and alter their own average lifespan, 01:51.2 and it's been due to many different factors: 01:55.2 due to changes in public health and cleaner drinking water; 01:57.2 due to refrigeration and preservation of food, and cooking; 02:02.0 it's due to the development of antibiotics, 02:06.2 the development of vaccines, 02:08.1 the development of anesthesia 02:09.2 so you could do surgery on people 02:11.2 and correct illnesses that way. 02:13.2 So anyway, this wonderful steady, steady progress of mankind 02:18.2 is unfortunately in some ways of thinking about it 02:22.3 a Road to Ruin, 02:25.2 because as we are curing these other diseases, 02:29.2 as we are living longer and longer lives, 02:32.2 we are finding that the incidences of neurodegenerative diseases 02:36.2 are going up. 02:39.1 These diseases used to be practically unheard of 100 years ago; 02:43.0 now, there's a very large fraction of people 02:46.2 around the world 02:48.0 that are suffering from these diseases, 02:49.2 and as we extend lifespan it's getting worse and worse. 02:53.0 There are five million Americans 02:55.3 suffering from Alzheimer's disease alone, 02:57.3 and this same increase in the disease 03:00.1 is occurring for all of the neurodegenerative diseases 03:03.1 across our globe. 03:05.1 So, unfortunately, 03:07.2 with respect to neurodegeneration 03:10.1 and it being a Road to Ruin... 03:12.1 this is why I say a Road to Ruin... 03:15.1 we're headed for neurodegeneration 03:17.1 and right now there's no exit. 03:20.1 We do not have a single therapy 03:22.1 that really fixes these problems. 03:24.1 So, these are some of the common and uncommon 03:27.0 neurodegenerative diseases you might have heard about: 03:28.3 Alzheimer's disease 03:30.2 and Parkinson's disease, 03:32.0 frontotemporal dementia, 03:33.1 HuntingtonÃ¢â‚¬â„¢s, 03:34.1 ALS, 03:35.2 and Creutzfeldt-Jakob disease. 03:37.0 And you can see these brown blobs inside of these cells, 03:40.3 and those brown blobs are aggregated proteins 03:44.3 like those aggregates of fried egg I showed you earlier. 03:50.0 And, as I said, 03:52.1 all of these neurodegenerative diseases 03:54.0 are protein folding diseases 03:55.2 and there's not a single therapeutic strategy 03:58.0 that cures the underlying protein pathology. 04:01.1 We have some things 04:04.1 that address some symptoms 04:06.0 in some of these diseases, 04:07.2 but for the most part we're pretty helpless against them. 04:11.0 So, I've been working on protein folding 04:14.1 for a long time 04:15.3 and I've worked on a lot of different organisms, 04:17.2 and the one thing that my studies over the years 04:20.1 has taught me 04:22.0 is that this problem, as I mentioned earlier, 04:24.0 is common to all organisms on Earth, 04:28.1 and so we got the kind of crazy idea that, 04:31.0 considering the Eukaryotic tree of life, 04:34.2 you see plants, animals, and fungi 04:38.2 actually split from each other not that long ago, 04:42.0 in terms of evolution. 04:45.2 So, we thought we might be able to take advantage 04:48.0 of this similarity 04:50.1 to study some of these really difficult, 04:53.1 really complicated diseases. 04:56.1 Yes, we will not be able to study 04:58.2 many different aspects of protein folding 05:01.0 in neurodegenerative disease 05:03.0 in a simpler organism, 05:04.3 but if we could study some aspects of the 05:07.2 precipitating, initiating protein pathology, 05:09.3 the cellular pathology, 05:11.1 not the complexity of the disease as a whole, 05:13.2 but just this initiating, precipitating pathology 05:16.1 from those proteins 05:18.2 in a simple organism, 05:20.0 we might be able to move much more quickly than we would 05:22.2 if we were confined solely to working on these 05:25.1 more complex organisms. 05:27.2 So, as I mentioned, 05:30.0 one of the things that we have in common with yeast 05:32.0 is a wide variety of systems 05:35.2 for controlling the protein folding problem. 05:38.0 So, we have chaperone proteins, 05:40.0 which interact with highly reactive proteins 05:45.0 that are not quite finished folding, 05:47.0 and prevent, just like human chaperones 05:49.0 prevent their charges 05:51.2 from interacting inappropriately with other partners 05:54.3 until they're reading and mature, 05:57.2 protein chaperones do the same thing. 06:00.1 But we also have protein remodeling factors, 06:02.2 things that can wrest those protein aggregates, 06:05.1 when they start to appear, apart, 06:08.1 we have osmolytes, we have things called the proteasome, 06:10.2 which degrades proteins that are not properly folded, 06:13.3 ubiquitin, ubiquitin ligases, 06:15.2 and that entire system is just completely conserved 06:18.2 from yeast to human cells. 06:20.0 But it's not just that. 06:23.1 Lipid biology is actually quite highly conserved. 06:26.0 There certainly are differences 06:28.2 in the lipid biology of yeast and human cells, 06:30.1 but, for example, cholesterol 06:33.1 #NAME? 06:36.2 for exactly the same reason that we use cholesterol: 06:39.1 to control the fluidity of membranes 06:42.2 and to control the movement and density of proteins 06:44.2 within those membranes. 06:46.3 And they move packages 06:50.0 of membrane-bounded proteins 06:52.1 around the cell in very highly orchestrated ways, 06:54.3 really the same way that a nerve cell 06:57.2 will move dopamine around, 07:00.1 the yeast cells will move things like mating factors around. 07:05.0 And lysosomes and peroxisomes, 07:07.2 these are very complex organelles 07:10.1 that are involved in very complicated functions, 07:12.1 some of them are involved in degrading proteins, 07:14.0 some of them are involved in 07:18.0 a wide variety of metabolic actions 07:19.2 that have to be segregated from the normal cytoplasm 07:21.3 #NAME? 07:23.3 They have autophagy. 07:25.3 This is a process by which the cell 07:28.2 actually directs its degradation and eating machinery 07:31.1 actually to eat up protein aggregates and to get rid of them. 07:35.0 Apoptosis, a programmed form of cell death. 07:38.1 The cell cycle, 07:40.1 a very complexly regulated cell cycle, 07:43.2 regulated very, very differently in bacteria, 07:45.1 but in yeast and humans, regulated in very much the same way. 07:48.0 And in fact studies of that cell cycle 07:50.0 were extremely important for our understanding of cancer 07:52.2 and why cancer cells start to replicate uncontrollably. 07:56.1 Studying them in yeast provides key insights. 07:59.1 We have mitochondria, the energy factory of the cells, 08:03.0 and mitochondria do amazing things in yeast and human cells, 08:06.1 but they also are a place where reactive oxygen species are generated 08:10.0 and can do a great deal of damage. 08:12.1 And then there's a whole variety 08:14.2 of signal transduction pathways, 08:15.2 again, these key pathways that 08:19.1 control growth and development in us, 08:21.2 but control responses to the environment 08:23.3 and responses to other cells, 08:25.1 and responses to internal and external stresses, 08:28.1 those same signaling pathways have been controlled... 08:32.3 have been preserved, rather, 08:34.2 in yeast and higher eukaryotes. 08:37.2 So, calcineurin is an example, 08:39.3 MAP kinase is, 08:42.0 G-protein coupled receptors... 08:43.3 all of these were first developed 08:46.3 long ago in eukaryotic life, 08:48.1 and greatly, greatly elaborated in us, 08:50.2 we have many, many more G-protein coupled receptors than a yeast cell has, for example, 08:54.3 but the basic machinery 08:57.1 and the basic concepts 08:58.2 and the basic ways in which those signaling pathways 09:00.3 drive processes inside the cell 09:03.1 are similar. 09:04.2 So, we got the idea that 09:07.1 maybe we could use those yeast cells 09:08.3 as our living test tube, 09:10.1 and the reason that we would want to do that is 09:12.2 there is no organism on Earth that we can manipulate 09:17.1 and get to tell us its secrets 09:19.0 better than yeast. 09:20.3 It has an absolutely unrivaled toolkit 09:22.2 and it really derives from brewers 09:26.2 back about 150 years ago wanting to make better beer, 09:29.1 and wanting to understand that organism 09:31.1 and how to manipulate it, 09:33.0 and it's taken off from there 09:34.2 and it's just amazing... 09:37.2 massive, massive numbers of people 09:39.2 have been building and developing technologies 09:43.2 that allow us to knock out every gene in the genome 09:46.2 or overexpress every gene in the genome, 09:48.2 make point mutants where we want in the genome, 09:51.2 and so that's just something that 09:54.2 we can't do in any other organism at this level today. 09:58.0 So, here's how we set things up. 10:01.1 We have yeast cells that are growing on... 10:04.1 in the top row, there... 10:06.0 they're growing on glucose medium. 10:08.2 In the bottom portion of the panel 10:10.2 they're growing on galactose media, 10:12.2 and we have a gene 10:15.2 that will turn on whenever we give the cells galactose. 10:18.0 And so we make a recombinant form of that gene 10:22.2 that now will, 10:24.0 instead of making the proteins 10:26.0 that yeast cells use for galactose utilization, 10:28.0 they make different proteins 10:31.0 that misfold in human diseases, 10:33.2 like ÃŽÂ±-synuclein, 10:35.1 AÃŽÂ², 10:36.1 TDP-43, 10:38.1 Huntington FUS. 10:39.3 And you can see that we've built... for ÃŽÂ±-synuclein here, 10:43.0 I've shown you three different strains 10:45.3 that are expressing the protein at different levels 10:48.3 and are exhibiting different levels of toxicity, 10:50.2 just by the fact that they can't grow very well. 10:52.2 And we've done that with all of these different disease proteins 10:55.3 and we've matched them so that they have the same level of toxicity, 10:59.1 so, same level of toxicity 11:01.2 from different proteins. 11:03.2 What it that? 11:05.0 Is this just some non-specific protein aggregation mess? 11:08.3 It turns out that it's not, 11:10.1 but when those proteins misfold inside of the yeast cell, 11:13.2 they go into the cell, 11:15.1 they interact with the same kinds of highly conserved constituents 11:18.1 that they interact with in a neuron, 11:20.2 and they do bad things in a very specific way. 11:24.2 So, here's an example of a phenotype. 11:27.1 The black glob over there 11:30.0 is protein nitration 11:32.0 and it's happening, 11:33.2 although the cells have the same level of toxicity, 11:35.2 the nitrational damage is happening 11:39.2 really only in the cells that are expressing ÃŽÂ±-synuclein. 11:41.3 That's really interesting because 11:44.1 in the human diseases that are known to be 11:47.1 caused by the misfolding of ÃŽÂ±-synuclein 11:49.1 -- and that is Parkinson's disease, 11:50.2 multiple systems atrophy, 11:52.0 Lewy body dementia, 11:53.2 and neurodegeneration with brain iron accumulation -- 11:57.0 they too show very high levels 11:59.2 of very specific protein aggregates 12:01.1 with nitration. 12:02.3 So, very unique and very specific cellular pathologies 12:05.1 directly related to the human disease. 12:08.1 So, here are our cells. 12:10.1 We've got this gene that we can turn on with galactose, 12:14.2 on and off with galactose. 12:16.0 And we've hooked it up to GFP 12:18.1 just so that we could see what's happening to it in the cells 12:20.0 as they were either healthy of dying, 12:23.2 and when we had just one or two copies of the protein in the cells, 12:26.2 they were fine and the protein went out to the membrane, 12:29.2 which is where it belongs. 12:31.2 And if we had more, just one extra copy, 12:35.3 we started to see things going wrong, 12:37.3 and if we had two extra copies 12:42.1 it went even worse, this does not look good, 12:43.2 these are protein conglomerates here 12:46.1 and some type of aggregation. 12:48.0 And then those cells grow fine, 12:50.0 those cells grow slowly, 12:51.2 and those cells die. 12:53.1 Very, very strong dosage difference, 12:57.2 and what's really interesting about that 12:59.3 is that's true in man as well. 13:01.2 Human beings that have one just one extra copy 13:03.3 of the wild type ÃŽÂ±-synuclein protein 13:08.1 will get early-onset Parkinson's disease, 13:09.3 and if they have two extra copies 13:11.1 they'll get an even earlier, 13:13.1 more virulent form of the disease. 13:14.2 So, this unusual, extreme sensitivity 13:17.2 to exactly how much protein you're making 13:19.2 was certainly reminiscent 13:22.1 of what was happening in man, 13:24.0 so how can we get a better idea 13:25.3 of what's going on here, 13:27.0 if there's anything really deeper involved. 13:29.3 Well, we do something called screening. 13:31.3 We screen every gene in the genome 13:35.1 for what makes cells better or worse, 13:37.2 we can take... with yeast, 13:39.1 we have libraries of every gene in the genome, 13:41.1 we can turn them up or turn them down 13:42.2 and see how that changes the disease manifestation. 13:47.2 And in these cells that have the 4 copies, 13:49.2 where they're just plain frankly dying of the disease, 13:52.1 we can screen for chemical compounds 13:54.2 that might rescue them 13:56.3 and studying those compounds 13:58.2 might tell us something about the disease pathology. 14:01.0 So, screening is a lot like panning for gold. 14:05.1 You go through a whole lot of stuff 14:07.2 and you look through it and you look through it and you look through it 14:10.1 and you find nothing for awhile, 14:11.3 and then all of a sudden you get these nuggets of gold. 14:14.0 So, out of the 6,000 genes in the yeast genome 14:17.2 that we studied, 14:19.2 only about 60 or 70 of them in our initial study 14:22.2 seemed to matter with respect to ÃŽÂ±-synuclein, 14:24.3 and the genes that we got out of our ÃŽÂ±-synuclein screens 14:29.1 were completely different from the genes that we got out of our AÃŽÂ² screens, 14:31.1 and completely different from the ones we got out of our Huntington's screen. 14:34.2 And they told us something about the biology 14:36.1 because, for example, 14:38.1 the largest class of genes we got were genes that are 14:41.0 involved in vesicle trafficking, 14:42.3 moving those membrane-bounded proteinaceous compartments 14:44.3 around the cell. 14:46.2 And so when I showed you 14:49.1 these protein conglomerations, here, 14:51.2 these aggregated forms of the protein 14:53.2 in this cellular model of the ÃŽÂ±-synuclein pathology, 14:58.0 it turns out that when we got that result, 15:00.2 that the genes that saved the yeast cells 15:03.2 from that pathology 15:05.2 were genes that were involved in moving little vesicles around, 15:08.2 we thought that, well, gee, 15:10.1 I wonder if those things actually 15:13.2 have something to do with vesicle trafficking? 15:15.1 And so when we looked at them 15:17.2 with the level of the electron microscope, 15:19.1 which allows a much, much higher resolution 15:21.1 view of the cell, 15:23.1 you can see that, yes, these little vesicles 15:25.0 that are packed with proteins in the cell, 15:27.2 depending on how much of the ÃŽÂ±-synuclein we're expressing, 15:31.0 we get more and more of these protein aggregates. 15:33.3 And then we did something called 15:35.3 immunoelectron microscopy, 15:37.2 where we attached a label to the antibody 15:40.2 against the ÃŽÂ±-synuclein 15:42.2 and against a protein involved in vesicle trafficking, 15:44.2 and we found that they were there together. 15:46.2 So, these blobs, these green blobs here, 15:48.2 are actually blobs, 15:51.1 not just of aggregated ÃŽÂ±-synuclein, 15:53.1 but aggregated ÃŽÂ±-synuclein 15:55.2 enmeshed in vesicles 15:58.0 that are not moving around the cell 15:59.2 and getting to the places they're supposed to be. 16:01.1 And when that happens in a nerve cell, 16:03.1 it's really disastrous, 16:04.2 because that's one of the major ways that nerve cells 16:06.2 communicate with each other. 16:09.0 That's not good for a yeast cell, either. 16:11.3 Anyway, this finding that ÃŽÂ±-synuclein 16:14.2 blocks vesicle trafficking 16:16.1 has now been corroborated by many other laboratories. 16:19.1 And to cut a long story short 16:21.2 and move on to the very final stage of this talk, 16:24.0 we found that there were parallel effects, 16:27.2 we moved back and forth between yeast and neurons, 16:29.1 and we found that there were parallel effects 16:32.0 on not just vesicle trafficking, 16:34.1 but bursts of reactive nitrogen species, 16:36.2 as I showed you in that protein blot, 16:39.0 mitochondrial dysfunction, 16:41.2 and perturbations in metal ion homeostasis. 16:44.2 So at least at this early, very simple cellular level, 16:48.1 there's a lot of similarity there. 16:51.2 But we really needed to be able to show 16:53.3 that the genes we found in yeast 16:55.2 and the genes that saved the yeast cells, 16:57.1 that those same genes would matter to a neuron. 16:59.1 So, we actually looked at this 17:01.3 in a couple of different system initially. 17:03.1 One was this nematode system, 17:06.0 it's a worm, it's a simple little worm, 17:08.2 but it's got lots of different kinds of neurons 17:10.2 and in fact it's got the same kind of neurons, 17:12.2 dopaminergic neurons, 17:14.3 that are adversely affected in Parkinson's disease. 17:19.0 And we could actually peer through... 17:21.1 wire up those cells to express ÃŽÂ±-synuclein 17:24.0 and wired them up so that they're green, 17:26.2 they glowed green, 17:28.1 we could actually study them in a living worm, 17:30.2 and we could see that when the worms 17:32.3 were expressing ÃŽÂ±-synuclein in those cells... 17:34.3 you can see how some of them are disappearing over there? 17:37.0 It's a true neurodegenerative model in the nematode, 17:41.3 and our genes that rescued the yeast cell 17:43.2 also rescued that nematode. 17:45.2 And the same thing happened when we took 17:49.0 neurons from rat brains, 17:50.3 the midbrain region of the rat, 17:53.0 which is the corresponding region 17:55.2 that's affected in Parkinson's in humans. 17:57.1 So that was pretty encouraging. 17:59.0 The next thing we did was to screen a chemical library, 18:01.3 and this again is something 18:04.2 that's so much easier to do in yeast. 18:07.0 We asked whether we could find compounds 18:09.1 that would fix more than one problem 18:11.1 -- I told you, there are lots of things going on, 18:12.3 there's a cascade of pathology 18:15.1 that gets kicked off by those misfolded proteins -- 18:17.2 and can we find compounds 18:20.2 that can fix more than one of those problems? 18:23.2 So... and the next question was, 18:26.2 can we use yeast genetics to find the target? 18:28.2 So, why would this matter? 18:30.2 Well, we could screen through, 18:33.1 and we did in fact screen through 18:35.3 500,000 chemical compounds, 18:38.2 asking for which ones were able to rescue the yeast cells. 18:42.3 That kind of a screen, which took us several months, 18:46.0 would take, I don't know, 18:48.2 maybe 100,000 years if you were using a mouse, 18:50.3 and also probably... 18:53.2 I don't know, billions and billions of dollars. 18:56.3 We did it much more cheaply and much more easily 18:59.1 and much more rapidly in yeast cells. 19:02.1 The other reason why it mattered was that 19:04.3 yeast cells offered, as I mentioned earlier, 19:07.1 unparalleled genetics. 19:09.1 And so we're actually able to take advantage of those genetics 19:12.2 to figure out what those compounds were doing 19:16.2 to save the yeast cell, 19:18.1 and then go back to neurons 19:20.0 and ask whether those same compounds would work in the neurons, 19:21.3 and whether those compounds would fix the same pathologies 19:25.1 that are taking place in the neurons. 19:27.1 And it worked. 19:29.1 So, we screened 550,000 compounds, 19:32.1 simply asked for restoring growth, 19:34.3 we don't... there are a lot of genes, 19:36.2 we don't know which one is the right one to try to go after, 19:39.1 we just looked for something that would restore growth. 19:42.1 And we've only dissected a few of these compounds so far, 19:44.2 but they ameliorate vesicle trafficking defects, 19:47.0 they ameliorate mitochondrial defects, 19:49.1 and they work, the ones we've tested, 19:51.1 in nematode, rat, and human neurons. 19:55.0 So, the final piece of this story 19:59.1 is to turn towards human iPS cells 20:01.1 made from patients that have one of these diseases. 20:04.1 This has been one of the most exciting aspects of... 20:07.2 revolutionary aspects of biology 20:10.0 in terms of being able to devise better treatments for patients, 20:13.1 but you can take skin cells from a patient 20:16.0 and actually dedifferentiate those skin cells 20:18.0 into an embryonic sort of state, 20:20.2 and then redifferentiate them into neurons. 20:23.1 And another amazing technology 20:26.2 that's been developed recently 20:28.2 by many other investigators 20:31.0 has been the ability to surgically genetically edit those cells, 20:34.3 such that you have corrected 20:37.1 just the mutation that's responsible for that person's disease, 20:40.1 and so you have absolutely genetically identical cell types here. 20:44.3 The only difference between them 20:47.3 is the difference that causes the disease, 20:52.2 and so then you can ask whether or not 20:55.1 you have any pathologies that are different between them 20:57.1 and whether any of the things that you've discovered earlier 20:59.1 work against those pathologies. 21:01.2 Except... the cells looked pretty much identical. 21:07.0 So, how do we figure out 21:09.0 what pathologies might be happening 21:10.1 because after all these pathologies 21:12.1 only manifest themselves in terms of human disease, 21:15.0 even in people who have these terrible mutations, 21:17.2 they only manifest at the ages of 40, 50, 60, 70 years old. 21:22.2 But we had the ability to go back to the yeast cells 21:26.2 and remember what we had learned from the yeast cells 21:30.1 and look for those same pathologies 21:33.0 arising in those cells long before they started to die. 21:35.3 So, long before they started to die, 21:37.2 we saw the same problems in vesicle trafficking, 21:41.1 the same problems in nitrosative damage, 21:43.2 nitrational stress, 21:46.0 and when... 21:48.1 and they did not happen in our mutant corrected cells, 21:51.1 so that allowed us to know that, 21:54.0 yes, not only were those pathologies happening, 21:56.0 but those pathologies were due to the mutation 21:58.0 that was causing that person's disease. 22:01.1 And then we went back and we asked 22:04.0 whether or not our compounds 22:06.1 could rescue those cells 22:08.0 and they could, at least the first few that we've tried have. 22:12.1 And we then used yeast genetics, 22:14.1 as I mentioned, 22:15.2 a lot of complicated experiments I won't take you all through, 22:18.1 but we used yeast genetics 22:20.1 to figure out what the target of the compound was. 22:22.3 And lo and behold, 22:25.1 it turned out to be a very, very highly conserved ubiquitin ligase, 22:27.3 you can see this is a cartoon of the different domains 22:31.3 of the ubiquitin ligase in the yeast cell, 22:34.2 known in yeast cells as Rsp5 22:36.2 and known in human cells as Nedd4, 22:38.2 and basically every domain is conserved. 22:41.0 And this is really an interesting kind of protein to find 22:44.1 because the ubiquitin ligases 22:47.0 are a very large, complex family in humans. 22:51.1 There are about 700 of them in humans; 22:53.1 there are about 300 of them in yeast. 22:54.2 And they've been very, very difficult for pharmaceutical companies to target 22:57.2 when they take the protein out of cell 22:59.2 and try to do what they have done traditionally 23:03.1 over the years, 23:04.1 which is try to find chemical compounds 23:06.1 that will alter the function of the protein in a purified system. 23:08.0 And that is because the biology of these proteins 23:10.3 really only manifests 23:14.1 within the context of a living cell, 23:16.2 where all the proteins are very, very close together 23:19.0 and crowded, and moving around, 23:20.2 and changing their conformational states. 23:23.0 That's where you see that this protein particularly matters. 23:27.2 And then this protein is a very complicated one 23:29.1 and it would have been impossible to find 23:31.3 without the kinds of simple chemical genetic methods 23:34.3 that we used. 23:36.1 So, it really demonstrates, 23:38.1 I think pretty strongly, 23:39.3 the power of phenotypic screens 23:41.2 for looking at compounds that really have 23:44.2 kind of very special properties, 23:46.0 properties that can correct the disease pathology, 23:49.0 and the power of chemical genetics 23:51.0 to figure out how those targets work. 23:52.3 Now, this is really only the beginning. 23:55.2 Whether this will ever turn out to be a therapy or not, 23:58.1 we don't know, 23:59.2 but what it has done is it has showed us that 24:02.0 this ubiquitin ligase plays a very key central role 24:04.0 in the way that pathology, that cellular pathology, 24:07.1 is manifested in lots of different ways. 24:09.3 And so it's certainly a useful tool. 24:13.2 We're finding more and more of the genes 24:16.3 that we found in yeast cells 24:18.2 are useful tools in understanding the biology, 24:20.3 and we hope one day, 24:22.2 maybe, 24:24.1 this will be a way of finding therapeutic compounds. 24:26.3 But what I do believe is that we need... 24:29.3 these diseases are very, very difficult, 24:31.3 and we need to try every trick in the book. 24:33.3 And so something as unconventional as this, 24:36.1 maybe it could provide one key. 24:41.3 So, we're pursuing the same sort of strategy 24:45.1 with Alzheimer's and other neurodegenerative diseases. 24:47.2 Many other people are pursuing other varied strategies, 24:52.2 but the reason I concentrated on 24:55.2 showing you what happens with one particular protein 24:58.0 was that, remember that idea 25:01.2 that maybe you could alter this heat shock response 25:03.3 and soup up the heat shock 25:06.2 in these neurodegenerative diseases 25:08.0 and take care of the protein folding problem, 25:10.0 well, our work with cancer told us 25:12.3 that might not be a good idea, 25:14.1 it might make the brain cells much more susceptible to cancer, 25:17.2 so that's why we're going after individual proteins in these diseases. 25:22.1 So, the first and final thing 25:25.3 I'm going to say 25:28.1 is that this work is 25:31.3 all thanks to the extraordinary group of people in my laboratory 25:35.1 who've been working with their whole hearts and souls 25:38.2 over the last many years 25:41.0 to try to understand the problems of protein folding 25:43.2 and protein homeostasis, 25:45.2 and try to figure out how understanding those problems 25:48.1 might be able to make a difference to mankind. 25:50.2 And I just want to say that I couldn't be 25:54.3 happier or more inspired 25:57.1 than to have worked with these people, 25:58.3 they're just amazing. 26:01.1 Each and every one of them has made a major contribution to this work. 26:04.1 Thank you very much.

Part 3: Hsp 90: a Driver of Novelty in Evolution

00:07.3 Hi there. I'm Susan Lindquist. 00:10.2 I'm at that Whitehead Institute at MIT 00:14.1 and in the Howard Hughes Medical Institute. 00:15.3 And I'm here to tell you about protein folding 00:18.2 and how it can act as a very powerful driver 00:21.2 for the evolution of novelty, 00:23.3 in a variety of different systems, 00:25.2 in a variety of different ways. 00:27.2 I'm going to tell you about our work in two parts. 00:30.3 The first part will be about Hsp90, 00:32.2 a very special protein 00:34.3 that helps other proteins to fold 00:37.1 and has multiple ramifications in terms of the evolution of novelty. 00:41.2 So, I'm going to start, perhaps, 00:44.2 by talking just a bit about this amazing man, here, 00:48.1 Dobzhansky. 00:50.2 In the 1930s, he really realized that we, 00:54.2 within the context of Darwinian evolution, 00:56.3 we really needed to have a new synthesis 00:59.2 that took advantage of some of the new insights in biology 01:02.2 that were emerging at that time. 01:04.2 And he really made a very coherent, 01:09.1 comprehensive 01:11.1 revamping of our understanding of evolution, 01:13.2 a tremendous influence on the field. 01:16.2 This is one of his most often quoted statements, 01:21.3 Nothing in biology makes sense 01:25.1 except in the light of evolution. 01:27.1 And that's absolutely true 01:30.1 and that's a mantra just about every biologist I know 01:33.2 lives and breathes by. 01:36.1 But he also interesting said this about the possibility, 01:40.1 the Lamarckian, 01:42.1 initially envisioned Larmarckian possibility, 01:44.2 that inheritance of traits 01:48.2 could be done in a rather different way 01:50.2 from what Darwin had originally envisioned, 01:52.1 and that would be that the environment 01:54.3 could influence the appearance of a new trait, 01:57.2 and that could then be passed on in subsequent generations. 02:01.2 And above that he was not... 02:04.3 Dobzhansky was not too wild about that concept 02:07.2 and he said, "This question has been discussed 02:10.1 almost ad nauseum 02:12.1 in the old biological literature... 02:14.0 so that we may refrain from the discussion of it altogether." 02:17.1 Well, what I'm going to tell you about today 02:19.1 and in the next lecture 02:21.2 is the fact that actually our understanding of how biological systems work, 02:26.1 and particularly how protein folding 02:28.2 influences those biological systems, 02:30.2 has actually provided 02:33.2 a perfectly rational and reasonable explanation 02:36.2 for how the environment 02:38.2 can lead to the appearance of new traits in evolution, 02:41.1 and it actually fits very nicely 02:43.3 within a Darwinian network of selection 02:47.2 and mutation 02:49.2 and variation. 02:52.2 So, let me tell you how I got into this. 02:54.1 We were working on something called the heat shock response 02:56.3 and this is it in a nutshell, 02:59.1 envisioned in three very, very different organisms, 03:02.3 a very, very highly conserved process. 03:04.3 So, what we have here are 03:07.1 yeast cells on the far end, there, 03:10.2 and Arabidopsis seedlings, 03:12.3 and human tissue culture cells. 03:15.1 In each case, 03:18.2 they started out identical, 03:20.1 but the top row was treated immediately 03:23.0 at a high temperature, 03:24.2 which killed them, as you can see. 03:27.0 The bottom row, those cells or plants, 03:30.2 were first exposed to a moderate, 03:35.0 very mild preconditioning heat treatment 03:36.3 for a short period of time, 03:38.1 for example, for the yeast it was 03:40.0 just half an hour at 39Ã‚Â°C, 03:41.2 a warm summer day temperature. 03:43.1 And that allowed those cells 03:45.2 to acquire an extraordinary ability to survive 03:48.2 much more extreme conditions, 03:49.3 and as you can see the same thing 03:51.3 applies for the plants, 03:53.1 it applies for the tissue culture cells, 03:55.1 it applies to every organism on the planet. 03:58.0 Birds do it, bees do it, even educated fleas do it. 04:01.1 Every organism responds to moderate stresses 04:04.1 by adjusting to the possibility 04:08.0 that there might be much worse stress coming along 04:10.1 and getting ready for it. 04:12.1 So, how do they get ready for it? 04:14.2 The way that they do it is actually to make 04:17.0 a variety of proteins called heat shock proteins. 04:19.3 So, these are revealed by simple 04:22.2 radiolabeling of cells at the normal temperature 04:24.2 -- this is yeast cells, 04:27.0 normal temperature is at 25Ã‚Â°C -- 04:29.0 versus that 39Ã‚Â°C conditioning treatment, 04:32.1 and you can see that what the cells are doing 04:35.1 is they've started to make a whole bunch of new proteins. 04:37.1 These proteins have been visualized by gel electrophoresis 04:41.0 and they've been spread out according to their molecular weight, 04:43.1 and they're named heat shock protein hsp104, 90, 70, 60, 04:48.3 just simply according to the sizes of the protein. 04:52.1 Now, it turns out that these are 04:55.2 induced by many different forms of stress. 04:57.2 They were first found in response to heat, 04:59.2 and that's probably a very, very common, 05:01.2 universal induction in nature, 05:04.1 but they're induced by all kinds of stressful conditions, 05:07.3 anaerobic conditions, 05:09.1 altered pHs, 05:11.1 changes in carbon metabolism 05:12.2 and food sources, 05:14.1 in energy, etc. 05:15.3 And they provide protection 05:19.1 against an enormous number of different stresses. 05:20.3 The proteins are very, very highly conserved, 05:24.1 such that you find these same proteins 05:27.1 being expressed not only in the organisms I've shown you, 05:30.3 which are all higher organisms, 05:32.3 but even going out way back to the 05:35.1 deepest lineages of organisms, 05:37.0 the prokaryotic organisms 05:41.2 known as eubacteria and archaebacteria 05:43.3 have heat shock proteins as well. 05:46.0 And it turns out that all of these proteins 05:49.1 are produced in massive quantities 05:51.2 after, very quickly, 05:53.1 a kind of an emergency response to stress. 05:56.2 All of them have to do with the protein folding problem. 06:00.1 So, what is the protein folding problem? 06:03.1 Well, this is an example of one protein. 06:06.0 The DNA code is long and linear 06:08.1 and it has to fold into this 06:10.0 very, very exact, precise shape. 06:11.3 This protein, when it's decoded from that linear DNA, 06:14.3 has to fold into a very precise shape to function, 06:18.1 and each protein folds 06:20.2 into a completely different shape. 06:22.0 And they do that in a just kind of crazy environment; 06:26.3 cells are absolutely crowded. 06:31.1 And so those proteins have to come out of the ribosome 06:33.2 in long linear strings 06:35.0 and fold up into the right shape 06:37.0 in the presence of a whole lot of other proteins, 06:39.2 and the thing about this 06:41.3 that's really quite extraordinary 06:43.2 is the kinetic energy, 06:45.1 because those proteins aren't just sitting there, 06:46.3 as they are in this static picture. 06:48.1 They're moving around like crazy, 06:50.0 they're banging into each other at an incredible rate. 06:51.2 And that means that it's hard for them to get folded in the first place 06:54.2 and it's hard for them to keep those folds. 06:56.2 All organisms face this problem. 06:59.1 Life is driven by the proteins 07:01.2 that are coded by DNA 07:03.1 and all of those proteins have to fold. 07:05.2 They fold in a chaotic environment 07:07.3 and stresses make that environment even more chaotic, 07:10.2 so it makes it more likely that those proteins 07:13.1 will start misfolding 07:15.0 and that's why they synthesize all these new heat shock proteins, 07:18.0 whose sole function 07:20.2 is really to help those other proteins stay folded, 07:23.3 keep folded, 07:25.2 and not get into aggregated messes. 07:28.3 So, what I'm going to be telling you about today 07:32.2 is about one particular member of the heat shock protein family, 07:35.3 and it's called Hsp90. 07:38.2 It's a particular interesting member 07:42.1 of this group of proteins 07:45.2 for a variety of reasons. 07:48.0 One is that it's 07:50.2 always far more abundant than is needed 07:52.2 under normal circumstances. 07:54.2 Every eukaryotic organism on the planet, at least, 07:58.1 that is, going from yeast to us, 08:00.2 is making much more of this protein 08:02.2 than it really needs under normal circumstances, 08:04.3 and that's actually not true for some of the other... 08:07.1 most of the other heat shock proteins. 08:08.3 Most of those are made at 08:10.1 just the levels that are needed and no more, 08:12.2 so that means that this excess Hsp90, 08:16.1 which is one of these protein folding aids, 08:19.3 is there to serve as a buffer against environmental stress 08:24.2 and a buffer against the protein folding problems 08:27.1 that occur with environmental stress. 08:30.3 And the other aspect of Hsp90 that's quite special 08:35.0 is that it has a tendency 08:36.3 to have a more restricted group of proteins that it helps 08:40.0 -- it doesn't help every protein in the cell. 08:41.2 It particularly helps proteins 08:43.2 that are known as signal transducers. 08:45.2 It helps others too, 08:47.1 but the main ones that it seems to concentrate on 08:49.0 are signal transducers. Signal transducers are proteins 08:52.2 that respond to just about everything in the cell. 08:55.1 They respond to developmental clues, 08:57.1 they respond to hormones, 08:58.2 they respond to stresses in the environment, 09:00.3 but they are what really drives 09:04.0 the biology of virtually every organism. 09:07.0 And so they're the major regulators, 09:09.2 there may be long pathways, 09:11.1 involving lots and lots of proteins, 09:12.3 but these signal transducers drive 09:16.3 whether that pathway will be on or not on 09:18.2 under different circumstances, 09:19.2 so they're very dynamic 09:21.1 and they're key to regulation of other cellular processes. 09:26.1 So, together, 09:27.2 the fact that there's this excess protein folding buffer 09:30.1 made up of Hsp90 09:32.1 and the fact that that buffer 09:34.1 is specifically directed towards these really dynamic, 09:36.1 really important, 09:38.0 central regulators of growth and development 09:40.1 creates a really interesting situation for evolution. 09:43.2 It allows Hsp90 to act as 09:47.0 both a potentiator and as a capacitor 09:49.2 for vast, vast amounts of genetic variation, 09:52.2 and we've found this to be true 09:54.2 in every one of those organisms I've just shown you, 09:57.1 from yeast cells to Arabidopsis seedlings 10:00.3 to human cells, 10:02.1 and in fact many other organisms in between. 10:06.3 So, by acting as a potentiator and a capacitor 10:11.2 for so much genetic variation 10:13.2 -- and it's not that other proteins 10:15.2 aren't acting to help other 10:18.0 genetic variants exert phenotypes, 10:19.3 this one just is a particularly powerful driver 10:22.3 of the effects of genetic variation -- 10:27.1 and that facilitates the appearance 10:29.2 of really complicated traits. 10:31.3 One of the problems in evolutionary biology is, 10:34.1 how can organisms rapidly evolve 10:37.0 what seem to be really quite complicated traits? 10:39.0 And there are many cases in evolution 10:40.2 where we know this has happened. 10:42.2 Evolution isn't always a slow, slow, gradual process. 10:46.0 And it's been hard to envision how that could happen 10:48.1 if each individual mutation 10:50.1 that contributes to that trait 10:52.0 has to occur independently 10:55.1 and then be selected for 10:57.0 and not make things untoward for the organism. 11:02.2 Hsp90 provides a way in which lots of variation 11:05.2 can be either potentiated or be buffered, 11:08.2 all at the same time. 11:10.1 And that causes that variation 11:12.2 to change with environmental stress, 11:14.2 because environment stress... 11:17.2 other proteins start to need Hsp90, 11:21.2 they take away that buffer, 11:23.1 and then you see the effects of the variation 11:25.0 in other parts of the genome. 11:26.2 So, that's the basic story I want to tell you, 11:30.0 so let me illustrate with several 11:33.0 different kinds of examples. 11:35.1 So, one of the first proteins that we looked at, 11:38.2 and in fact the first one that showed us, 11:40.2 gave us a hint about the power of this protein 11:43.0 in terms of evolution, 11:44.2 was a protein called v-Src. 11:46.1 v-Src is actually the first oncogenic protein found, 11:50.0 that is, the first cancer-causing protein found, 11:53.2 and it's a mutant version of the normal cellular protein 11:56.2 called c-Src. 11:58.2 Well, we'd be studying this heat shock response 12:00.2 for a variety of other reasons 12:02.1 and we realized that, 12:04.2 oh, it's so highly conserved, 12:07.3 and so we realized that maybe... 12:11.2 people had found v-Src associated with Hsp90 12:14.1 and they didn't really know quite what it was doing 12:17.2 associated with Hsp90. 12:19.2 They had found it associated with Hsp90 but... 12:22.3 of course, this was in human cells and rat cells and higher organisms 12:26.1 that are prone to cancers, 12:28.0 but because this whole system of heat shock proteins 12:30.2 is so highly conserved, we thought, 12:33.0 well, maybe we could test what that relationship 12:35.1 between Hsp90 and this cancer-causing protein was 12:38.2 by using the yeast cells as a sort of 12:41.3 a living test tube. 12:43.1 They have all the crowding 12:44.2 and all those protein quality control issues 12:47.0 that our higher organisms have, 12:49.0 but they're really simple and easy to use, 12:50.2 easy to work with 12:52.3 and manipulate genetically. 12:55.2 So, we took this cancer-causing protein 12:58.2 and we put it into our simple little yeast cells, 13:01.2 and the reason why we did that was that 13:05.1 we could make yeast cells, genetically, very easily, 13:07.3 that had normal, high, 13:10.2 lots of abundant Hsp90, 13:12.2 or lower levels of Hsp90. 13:15.0 That was the lower levels that could take care of 13:16.3 all the lower functions needed. 13:18.2 The cells couldn't grow at higher temperatures, 13:20.0 but they were fine at normal temperatures. 13:22.1 And just see, well, 13:24.1 what was the difference in the behavior of this protein 13:26.1 that associated with Hsp90 13:28.1 when there was a difference 13:31.3 in the level of Hsp90? 13:34.1 It had actually previously been thought that 13:37.1 the association between this protein and Hsp90 13:40.1 was keeping that kinase inactive, 13:42.3 because in the complex with Hsp90, 13:44.3 when it was purified, 13:47.0 and this was done with several different kinases, actually, 13:49.1 in several different laboratories, 13:51.0 that oncogenic protein, 13:53.2 which is a kinase and modifies other proteins 13:56.2 which cause growth, 13:58.3 it was inactive. 14:00.3 So it naturally was assumed that 14:03.0 Hsp90 was inhibiting it. 14:05.2 So we tested that in our yeast cells 14:07.3 which had low levels of Hsp90. 14:09.2 If Hsp90 was inhibiting that protein, 14:12.1 now it should become much more active. 14:14.2 And we got exactly the opposite result. 14:17.0 So, first you can see, on the left over there, 14:19.2 that we've got... 14:21.1 simply looking at the level of v-Src expression 14:23.2 that we've engineering into these yeast cells, 14:26.0 and with our wild type cells 14:28.2 or the cells that have reduced levels of Hsp90, 14:30.2 it's pretty similar. 14:32.3 There's not much difference between them. 14:35.0 But when we assayed the oncogenic activity of that kinase, 14:38.1 its ability to modify other proteins with phosphate 14:41.3 and use those phosphate modifications 14:44.1 to change the biology of the cell, 14:46.1 and in human cells fuel cancer and malignancy, 14:50.0 it was when we reduced Hsp90, 14:52.0 the activity of that protein didn't increase. 14:55.3 It was completely dead as a doornail. 15:00.0 And the evolutionary aspect of this 15:03.3 became clear when we said, 15:05.3 well, let's look at the normal cellular kinase. 15:10.2 And there we had a really interesting result. 15:13.0 So, when we put the cellular version of the kinase into yeast cells 15:17.2 and we monitored activity... 15:19.0 now, we had to expose this blot 15:20.2 for about 20 times longer 15:23.1 than we had exposed the oncogenic cancer-causing blot, 15:26.1 because the normal protein 15:29.3 is of course much less active... 15:32.2 but when we exposed it 20-fold longer 15:35.1 and we were actually able to see the activity 15:37.1 under the two different circumstances 15:39.2 -- wild type levels or low levels of Hsp90 -- 15:42.2 there wasn't much of a difference. 15:44.0 There was a little bit of a difference, but not much. 15:47.2 So, that indicated that Hsp90 was particularly important, 15:52.1 this buffer, this excess amount of Hsp90, 15:55.0 was particularly important 15:57.2 for that cancer-causing protein, 16:00.2 that newly mutated protein, to be active. 16:05.0 So, is this just an oddity of yeast cells? 16:07.2 No, it turns out that 16:10.1 someone who has been a wonderful colleague 16:13.2 and friend of mine over many, many years, 16:17.0 Luke Whitesell, 16:19.1 did a very, very interesting experiment in mammalian cells. 16:23.3 So, he found that geldanamycin, 16:26.2 an Hsp90 inhibitor, 16:29.1 reverses the effects of this cancer-causing protein 16:32.2 in human cells. 16:35.2 So, here are cells that are... 16:39.1 the control cells are growing out of control 16:41.3 because they're expressing 16:44.1 this cancer-causing protein 16:45.2 that's causing them to grow too much 16:47.1 and they're just kind of piling up all over the place 16:49.2 out of control. 16:51.0 When the cells are exposed to geldanamycin, 16:52.2 which brings down that Hsp90 buffer, 16:54.3 it inhibits it, 16:57.3 they slow down, they spread out, 17:01.0 they get contact-inhibited, 17:02.3 they're normal. 17:04.3 The interesting thing about this experiment was that 17:08.1 it was initially not realized 17:11.0 until Luke did some really interesting biochemistry 17:13.2 that that's what he was doing with this geldanamycin. 17:16.2 When he first bought this 17:18.1 #NAME? 17:20.0 of how science keeps turning things upside down -- 17:23.3 when he first bought geldanamycin, 17:26.2 it was advertised in the Sigma catalog as a Src inhibitor, 17:30.1 and that's why he had applied it to these cells, 17:32.3 because he wanted to see whether inhibition of Src 17:34.3 would reverse the cancer phenotype. 17:39.3 But when he purified Src 17:43.2 and added the compound 17:46.3 and looked at its kinase activity on a gel, 17:50.1 there was absolutely no difference. 17:52.3 That compound was not inhibiting the kinase. 17:55.3 So he put the compound on a column 17:58.2 that allowed us to fish out the proteins 18:01.3 that that compound was binding to, 18:04.2 and as you can see here on this gel 18:06.2 there's one protein that that compound is binding to 18:09.2 #NAME? 18:10.2 The first inhibitor of Hsp90 found, 18:12.2 and it allowed this test, then, 18:15.2 of, is Hsp90 buffer 18:18.2 serving that same function in these human cells? 18:20.1 If you reduce that excess amount of protein, 18:23.0 does it prevent that new phenotype 18:25.1 that was caused by that new mutation? 18:28.1 So, with a long series of experiments 18:31.1 by several other laboratories, we now... 18:34.1 and I especially want to point out Johannes Buchner, 18:37.0 this is not easy biochemistry to do 18:39.1 and Johannes Buchner's group did a lot of it... 18:43.0 the picture we have is now much, much clearer. 18:46.1 So, Src is a protein that sits in the cellular membrane 18:50.0 and it normally folds back on itself 18:52.2 and keeps itself auto-inhibited. 18:55.0 And it only signals to cells to grow 18:57.1 every once in a while, 18:58.3 in the normal conditions where they're supposed to grow. 19:01.1 What happens in those cancer causing mutations 19:04.0 is that the protein can no longer fold properly, 19:08.2 because that part that tucks into itself 19:13.0 and keeps it folded back on itself and inhibited 19:15.1 is no longer working. 19:17.3 So the protein unfolds, 19:20.1 it's very, very unstable, 19:22.2 and it would normally just get degraded or aggregated in the cell 19:25.1 and not be good for anything. 19:27.1 Hsp90, however, 19:30.0 sees it as a protein that's in trouble, trouble folding. 19:32.3 It comes it, in swoops in, 19:34.1 oh, you're just the kind of protein I'd like to bind to, 19:36.2 a signal transducer that activates cellular pathways, 19:40.1 so I'm going to help you fold. 19:42.1 So, it helps the protein fold, 19:44.1 helps it get to the membrane, 19:45.3 and keeps it active. 19:47.2 So, Hsp90 in this case, 19:49.1 this excess buffer, 19:51.1 has been absolutely essential for that new mutation 19:53.2 to creates this very new phenotype, 19:57.0 the oncogenic state. 19:58.3 And that has turned out to be true 20:01.0 for many, many other kinases, now, 20:03.2 that cause cancer. 20:05.0 So it's a very broad truth 20:08.3 in the cancer kinase literature 20:11.2 that Hsp90 is important, 20:14.0 and because it's just... 20:16.2 you can inhibit that oncogenic transformation 20:20.1 by just reducing the buffer, 20:23.1 not eliminating the protein but just reducing the buffer, 20:28.2 so that it doesn't hurt other proteins in the cell, 20:32.1 the idea has been that this might be 20:35.0 a very useful clinical tool 20:38.1 in a variety of different cancers, 20:40.0 and that's being tested by many pharmaceutical companies now. 20:44.1 So, what about a completely different kind of 20:48.1 evolutionary process? 20:50.3 We turned here to a very different group of organisms 20:54.1 -- we turned to fungi -- 20:57.2 but we also wanted to study something 21:00.1 that was also maybe important for human biology and medicine, 21:02.3 and for which there were a lot, 21:04.2 because it was important for human biology and medicine, 21:06.2 there were a lot of tools that we can take advantage of 21:08.2 in terms of studying evolutionary processes. 21:11.1 So, we chose to study 21:14.1 the evolution of drug resistance in fungi, 21:16.2 because fungal infections are a horrific problem. 21:20.2 These take a variety of different forms, 21:25.0 but when fungal infections becomes systemic, 21:27.2 when they've really invaded us, 21:29.1 there is very little that we can do about it 21:32.1 and the mortality rates are 40-90%, 21:35.3 depending upon the fungus and the circumstances. 21:39.2 The reason why the majority of the mortality rates are so high 21:42.2 is there actually are very few antifungal agents that we have, 21:47.2 there are really only three major ones 21:50.1 that are deployed in the clinic right now. 21:53.1 The reason for that is very interesting. 21:54.3 Even though you would think of fungi in human beings 21:58.0 as being very, very unrelated to each other, 22:01.0 it turns out that their biology, 22:02.1 if you look at the evolutionary tree... 22:05.0 the eukaryotic lineage, 22:07.0 which split off from archaebacteria and eubacteria 22:09.1 a long, long time ago, 22:11.1 and then there was a whole lot of evolution that took place 22:14.2 before fungi and plants and animals 22:17.2 split off from each other, 22:20.2 and so there haven't been very many selective targets... 22:23.0 there's a lot of antibiotics that target things about bacteria 22:25.1 that have nothing to do with a human being, 22:27.2 but there haven't been that many 22:30.0 successful ways of targeting things 22:32.0 that are specific to fungi 22:33.2 that are not found in human beings. 22:35.2 So, evolution... 22:38.1 resistance is evolving, it's becoming a big problem, 22:40.0 and we thought it was worth studying. 22:43.1 So, here, what we've done is a very simple experiment. 22:46.2 We've taken Candida albicans... 22:48.0 we did this actually with the lab rat yeast 22:52.1 Saccharomyces cerevisiae first, 22:54.1 because genetically you can manipulate it much, much better. 22:56.1 Candida albicans is a pain in the neck, 22:58.1 actually, to manipulate, 23:00.2 but it's a much more important human pathogen. 23:04.0 So, what we've done here is we've simply plated it out onto Petri dishes, 23:09.0 and you can see the cells are growing like crazy over here, 23:14.0 and then we've put in this antifungal drug 23:16.1 called Fluconazole over here, 23:18.0 and you can see that most of the cells have died, 23:20.2 but not all of them. 23:22.1 And in fact you can start to see colonies 23:24.1 growing stronger and stronger and stronger 23:26.2 that are evolving resistance to that drug. 23:30.1 So we asked, 23:32.1 does this kind of resistance 23:34.3 that's evolving in the laboratory, 23:36.2 does that also, does that new phenotype 23:39.0 depend upon this Hsp90 buffer? 23:41.2 So we took advantage of some of these compounds, 23:44.1 like that one I just told you about, geldanamycin, 23:46.0 and radicicol, 23:48.1 compounds that very selectively inhibit Hsp90, 23:51.1 and we asked, well, 23:52.3 what if we just put in a smallish amount of those compounds, 23:55.1 such that we took down that excess buffer of Hsp90 23:58.2 protein folding capacity. 24:01.1 What would happen to the evolution of drug resistance? 24:04.2 And what you can see here is that 24:07.0 we're using a concentration of that Hsp90 inhibitor 24:11.0 that has no effect on the normal cells 24:12.3 under normal circumstances 24:14.3 -- they're just growing perfectly happily 24:17.1 because they don't need that much Hsp90 function 24:20.0 for normal growth -- 24:22.1 but they need it to evolve drug resistance. 24:25.2 Now, there are some mechanisms by which 24:27.2 they can evolve drug resistance 24:29.1 that don't depend on Hsp90 in the laboratory, 24:31.3 but an awful lot of those different mechanisms 24:35.0 do depend upon Hsp90, that protein folding buffer, 24:38.1 to allow these new mutations 24:40.2 to create their new phenotypes. 24:43.1 So, we got exactly the same results 24:46.2 with two completely structurally unrelated inhibitors 24:50.0 which had in common the fact that 24:53.0 they both inhibited Hsp90, 24:54.3 and so that, 24:56.3 when that really important aspect of the chemical proof, 25:00.1 the experimental way of working with this organism 25:03.1 that was so difficult to work with genetically 25:05.3 was to take these two very different drugs 25:07.1 and find the same effect, 25:09.2 we then later, subsequently, 25:11.1 were able to do it genetically, 25:13.1 but that took a couple of years. 25:15.0 Anyway, this is a laboratory experiment 25:17.2 and evolution doesn't take place in a laboratory. 25:20.2 What about evolution in a real life situation? 25:25.3 So, in this case we took advantage of some colleagues' 25:30.1 wonderful work 25:32.2 that had isolated strains of Candida albicans 25:36.3 from patients who were suffering from fungal infections. 25:40.2 And the particular series of strains 25:42.3 that I'm showing you over here 25:44.3 is a series of strains that... 25:46.3 well, the top one is the laboratory strain 25:50.0 and each row below that 25:52.2 is a clinical isolate taken out of a patient 25:55.1 over the course of about two years. 25:57.2 Now, the reason why this is an evolutionary experiment 26:00.2 is that when the patient got better 26:02.3 and then got sick again, 26:04.2 it turned out that it wasn't some new fungus 26:07.1 invading that person from the environment, 26:09.1 but rather that fungus inside that patient 26:11.2 had gotten under control from the drugs, 26:15.1 but then it rebloomed. 26:18.0 And so it went through this series of being under control, 26:20.3 blooming, under control, blooming... 26:22.3 so it's an evolutionary series, 26:24.3 so that's what we call in vivo selection. 26:26.2 This is a natural case in which the cells are evolving. 26:29.2 Now, you can see the green means growth 26:31.1 and the black means no growth, 26:33.0 and you can see that under 26:34.3 all of the different concentrations of fluconazole, here, 26:37.1 these cells are actually doing pretty well. 26:39.3 So, even the initial isolate from the patient 26:43.1 was fairly resistant 26:45.1 and you can see it's getting a little greener down there, 26:47.0 it means that the cells are growing even better 26:49.0 in the presence of the drug. 26:51.0 Did this naturally occurring 26:54.1 evolution of a new trait in the human being, 26:58.1 the natural, real life evolutionary experiment, 27:02.1 did the acquisition of that new trait, drug resistance, 27:06.0 depend also upon that Hsp90 protein folding buffer 27:09.1 to enable those new mutations 27:11.2 that caused resistance to create this novel phenotype 27:15.0 of drug resistance? 27:17.1 Again, we used those inhibitors 27:19.2 and we just brought down the buffer, 27:21.1 Hsp90 buffer a little bit, 27:23.1 and what we found was that in the absence of fluconazole, 27:27.1 those... the buffer... 27:29.1 we're not inhibiting Hsp90 all that much, 27:31.0 because in the absence of fluconazole the cells 27:33.1 are growing just perfectly fine, 27:35.1 but as the concentration of fluconazole is increased, here, 27:39.1 you can see that eliminating that Hsp90 buffer 27:42.2 has completely done away with their resistance. 27:46.2 Their resistance depended upon access 27:48.3 to protein folding capacity. 27:52.1 Now, the other thing that was interesting here 27:54.2 was that you can see that as time went on, 27:56.3 the cells evolved very robust resistance 27:59.1 that was independent of Hsp90. 28:01.2 Towards the end of this person's life, 28:04.1 that organism had become so virulent that the... 28:07.2 and had acquired new mutations, 28:10.1 and it was no longer sensitive to that inhibition of Hsp90. 28:16.0 So, what could have driven that, well, remember... 28:19.0 drove that evolutionary process such that 28:21.3 something that was originally a trait that was originally dependent upon Hsp90 28:24.1 then became over the course of time independent of it, 28:27.0 became fixed in effect? 28:30.3 Well, remember that these heat shock proteins 28:33.2 are taking care of environmental stresses 28:36.1 and the protein folding problems 28:38.0 that occur with environmental stresses. 28:39.3 So, it occurred to us that fever temperatures, 28:43.1 I mean, 28:45.1 we are always naturally mounting fevers in response to infections, 28:47.2 maybe... one of the reason for that 28:49.3 is those fever temperatures create protein folding stresses 28:51.3 in the organism... 28:53.1 that might use up that Hsp90 buffer 28:55.1 and also take away the drug resistance. 28:57.2 And sure enough, that's what happens, 28:59.2 at least when we look at those strains and examine them in a laboratory. 29:03.2 These do not have the Hsp90 inhibitor, 29:05.2 there's something growing at higher temperatures, 29:08.1 and those higher temperatures, we think, 29:10.3 are what contributes to the selective pressure 29:13.0 over the course of this patient's 29:15.1 terrible infection history 29:18.1 to create a driving force for new mutations 29:20.2 to now allow that resistance 29:23.3 to be maintained, 29:27.1 even when Hsp90 functions 29:30.2 and other protein folding homeostasis functions 29:33.2 are stressed. 29:36.3 It's turned out that we've now studied 29:39.0 this same process in fungi 29:42.1 that have diverged from each other 29:44.1 by a billion years of evolution, 29:45.3 that are very, very different organisms, 29:48.2 they're as different from each other 29:51.1 as they are from us, to be honest, 29:53.2 and we've looked at it in response to 29:56.0 three very different kinds of antifungals 29:58.2 that cause the evolution of resistance, 30:02.1 and we've found basically the same thing. 30:05.1 When we look at strains 30:08.1 that have naturally evolved resistance against 30:12.1 these drugs in human patients 30:14.2 and then ask whether that evolution 30:17.1 depended upon the Hsp90 buffer, 30:19.2 in around about 50% of the strains 30:22.0 that is indeed the case, 30:24.1 so that's a lot of evolution, 30:26.0 covering a lot of territory. 30:29.1 So, those are two examples 30:33.3 of Hsp90 acting as a 30:39.1 potentiator for new mutations, 30:41.2 this protein buffer allowing new mutations 30:44.0 to create new phenotypes. 30:47.0 And these new phenotypes are lost, 30:50.0 transiently, 30:51.2 with stress and additional mutations 30:54.1 that can accumulate, however, 30:56.2 can lead to their fixation. 31:01.0 There are, it turns out, 31:03.2 multiple mechanisms involved. 31:05.1 I showed you one mechanism by which the Hsp90 buffer, 31:08.1 that protein folding agent, 31:09.3 helps to drive the phenotype of an oncogenic kinase. 31:13.1 It turns out that for these other drugs, 31:15.1 the nature of the mutations 31:17.1 and the nature of the ways in which they create resistance 31:20.0 are quite varied, very diverse, 31:22.1 and Hsp90 acts in those pathways 31:24.3 in a variety of different ways, 31:26.2 but it is a really powerful driver 31:28.3 of this evolutionary novelty. 31:31.3 And that you can look at some of my papers 31:33.1 if you want to read the details, 31:34.3 I don't want to go into any of those details now. 31:36.2 Because I want to turn to something else. 31:38.2 I want to turn to the fact that 31:40.3 Hsp90 can also serve as a capacitor 31:44.1 for genetic variation, 31:46.0 and when it serves as a capacitor for genetic variation, 31:49.2 what it's doing is kind of exactly the opposite. 31:53.3 Having this excess protein folding buffer 31:55.3 allows lots of polymorphisms and genetic variants 31:58.3 to accumulate without any effect on the organism, 32:01.2 you don't notice them, 32:03.2 but then when stress, 32:06.2 environmental stress or internal stress, 32:09.3 causes problems with protein homeostasis 32:12.1 and starts to use up that Hsp90 buffer... 32:15.2 wow, the effects of that genetic variation 32:19.0 now become very important. 32:20.2 And so, in those cases, 32:23.1 those are traits that appear under stress, 32:27.2 so quite different from the way in which 32:30.2 things behave with respect to potentiation by Hsp90. 32:34.3 So, let me maybe show you, 32:38.1 instead of just starting with the results that we got, 32:40.2 which initially were a little bit baffling to us, 32:43.1 let me sort of place it in the context 32:45.3 of a much broader literature. 32:48.2 This is a signal transduction diagram of a cell, 32:52.2 of a cancer cell in fact, 32:55.1 and signal transduction diagrams 32:58.1 are really like circuit diagrams on a computer chip, 33:02.2 and they direct the flow of proteins around the cell 33:06.1 and the behavior and activity of proteins 33:08.1 around the cell. 33:09.2 And you can see that, for example, 33:11.1 here are growth factors coming in, 33:13.0 it's binding to a protein, 33:14.2 and it's sending all kinds of signals 33:17.2 along the cell 33:19.2 and causing all kinds of things to happen. 33:21.1 The arrows indicate 33:25.1 the key signal transduction steps, 33:27.0 the key proteins that depend upon Hsp90. 33:32.2 So, Hsp90 is helping to drive, under its normal circumstances, 33:37.2 Hsp90 is helping to drive all of these pathways forward, 33:40.3 and so you can imagine that 33:43.1 if something happens to inhibit Hsp90, 33:45.2 and that could happen either pharmacologically 33:49.0 or just environmental stress 33:51.1 is using up that Hsp90 buffer 33:54.2 and not making as much of it available any more, 33:57.2 those pathways might not work quite as robustly, 33:59.2 might not go quite as robustly. 34:01.2 And for that reason genetic variation 34:04.2 that was in those pathways 34:06.1 that wasn't having an effect previously, 34:08.2 because those pathways aren't working so well anymore, 34:11.2 those variants can exert all kinds of effects 34:15.2 and cause all kinds of new phenotypes. 34:18.1 So, how did we get there? 34:21.2 Well, we started with some Hsp90 mutations in fruit flies, 34:25.2 and this is an example, a wonderful example of 34:30.0 how laboratories that create 34:35.1 new tools and new materials 34:38.1 can sometimes really drive forward 34:41.0 the progress of science 34:43.0 by being generous with their materials and sharing them 34:45.0 with other laboratories. 34:46.2 So, Gerry Rubin and Ernst Hafen's laboratories 34:49.1 had both found Hsp90 mutations in fruit flies, 34:53.1 and they found them because 34:55.2 they were interested in studying two different signal transduction 35:00.0 pathways in different contexts in the fruit flies 35:02.0 that drove developmental processes, 35:03.3 and, not surprisingly given what I've told you, 35:07.0 mutations in Hsp90 can affect those developmental processes 35:10.0 and those pathways. 35:12.0 But they weren't interested in Hsp90 per se, 35:14.0 so they were very, very generous 35:16.0 and they gave those mutations to us. 35:18.1 What we found was that, 35:21.1 what they had found initially, 35:22.3 was that if that if the flies... 35:25.2 and of course they're a diploid organism, so they have two copies of Hsp90... 35:28.0 if both of those copies of Hsp90 are mutant, 35:30.2 the organism dies. 35:32.1 Hsp90 is a very important protein. 35:34.0 They can't do without Hsp90. 35:37.1 If one of the copies is bad, 35:40.2 they're pretty much fine, 35:42.1 except that we noticed that they were 35:45.1 only mostly normal, 35:47.0 and that what we found was that 35:49.2 a small number of the flies, 35:51.1 one or two percent of the flies, 35:52.2 had all sorts of different phenotypes, 35:55.1 I mean, hundreds of different phenotypes. 35:57.2 They had eyes that were growing off on stalks, 36:00.3 they had bristles in the wrong places, 36:03.2 they had wings that had notches in them, 36:06.1 they had just all kinds of crazy phenotypes. 36:08.2 And I had at this time been working on Drosophila for a while, 36:12.1 and I had never seen effects of a mutation 36:15.2 have so many pleiotropic effects 36:18.0 and create so many different phenotypes in the fly, 36:21.2 especially when only one copy of the protein was mutated. 36:28.0 So, we get new traits when Hsp90 is inhibited 36:32.2 and here are even more examples of new traits. 36:37.2 And what we found was that Hsp90 inhibition, 36:44.2 so, this was genetics inhibition of Hsp90, 36:50.1 one mutated copy of Hsp90, 36:51.2 so you've reduced the buffer, 36:53.1 you've reduced the excess amount of Hsp90, 36:56.1 that compromise of Hsp90 37:00.0 was revealing the effects of hidden genetic variation. 37:01.3 So, it wasn't just destabilizing development 37:06.1 in a random way, 37:07.3 and the way we found that out was that 37:10.2 it turns out that in different strains of fruit flies, 37:14.1 when we crossed those same Hsp90 mutations 37:18.1 into different strains of fruit flies, 37:19.2 we got completely different phenotypes in those strains 37:22.2 because there was genetic variation in those strains 37:26.1 that affected 37:30.1 what Hsp90 was able to do. 37:34.1 We also found that, in addition to genetic experiments, 37:37.2 one way of really... 37:40.0 this was kind of a crazy result at first, right, 37:42.2 and so we wanted to be sure that was due to Hsp90 inhibition, 37:45.0 and so we used a variety of tools. 37:47.1 I showed you the genetic one. 37:48.3 We also used those pharmacological tools 37:50.2 to inhibit the Hsp90 buffer, 37:52.1 got the same effects. 37:54.2 And then we said, well, again, 37:56.3 maybe this has got something to do with... 38:00.0 is a way of connecting environmental variation 38:04.2 to the appearance of new phenotypes, 38:06.0 so we asked whether simply growing the flies 38:08.1 at higher temperatures 38:10.1 would reveal those same phenotypes, 38:12.0 those same new traits. 38:13.2 And indeed, when protein homeostasis buffers 38:17.1 were compromised by higher temperatures, 38:19.0 we saw the same traits. 38:21.3 The cool thing was that those traits, 38:24.2 because they depend upon 38:27.2 preexisting genetic variation, 38:29.1 the uncovering of preexisting genetic variation, 38:33.0 they can be assimilated 38:36.0 by reassortment of that genetic information 38:38.1 in the next generations. 38:41.1 So, let me show you how this works 38:43.0 in a simple cartoon form. 38:45.2 We start with a population of flies 38:47.2 that have a lot of different genetic variation in them 38:50.2 that might contribute to a change in an eye phenotype, 38:55.2 but none of the flies have that phenotype 38:57.3 -- you don't see anything. 39:00.1 When you compromise the buffer of Hsp90, 39:02.0 now a small number of those flies, 39:05.2 1%, as I said, or half a percent 39:09.1 or sometimes 2%, 39:11.1 but it was only a small number of the flies, 39:13.2 suddenly saw a new phenotype. 39:16.2 And then what we did was 39:20.0 we mated the two different flies 39:23.2 from lots and lots of bottles of flies, 39:26.0 we mated pairs that had the same phenotype together 39:30.1 and we, of course, now understand that 39:35.0 what was happening was we were selecting individuals 39:37.0 that happened to have quite a bit of this genetic variation 39:40.1 that would cause a change in the eye morphology 39:43.1 when the Hsp90 buffer was reduced. 39:45.3 And in that very first generation, 39:49.0 now instead of being in about 1% of the flies, 39:52.0 it got up to about 2 or 3%, 39:53.3 and then the next generation, 39:55.2 when we did that cross, 39:57.1 it got up to 6 or 7%, 39:58.2 the next generation, 40:00.1 it got up to 10 or 12%. 40:02.0 It took a long time, 40:03.3 but after several generations of selective breeding 40:05.3 the flies in the subsequent generations 40:08.2 had enough of that genetic variation 40:10.3 that they had that eye phenotype 40:14.1 whether they were grown at high temperatures, 40:16.2 whether they had the Hsp90 mutation, 40:19.1 or whether they were pharmacologically inhibited. 40:21.2 It didn't matter. 40:22.3 This trait which initially appeared 40:25.1 because of a compromised protein folding buffer, 40:28.2 in response to environmental stress, 40:31.2 became fixed in subsequent generations 40:33.2 by the recombination of that genetic variation 40:36.2 to create a robust new phenotype. 40:39.2 So, then we decided to go back to yeast, basically. 40:43.0 The reason for wanting to go back to yeast was 40:45.2 because this organism, as I mentioned before, 40:48.2 can be manipulated, genetically, 40:50.1 better than any other organism on the planet right now, 40:53.3 and that's actually due to the fact that 40:56.3 beer brewers many, many years ago 40:59.0 wanted to make better beer, 41:00.2 and so they started to manipulate the genetics of yeast. 41:03.1 And it's just become a phenomenally important 41:07.2 and powerful model organism. 41:10.2 Now, we wondered whether, 41:13.2 in naturally occurring yeast strains 41:16.0 from all sorts of different environments, 41:18.2 we might see how broadly this effect 41:23.1 of the Hsp90 buffer is 41:25.2 on the manifestation of genetic variation. 41:27.3 So, we were able to get strains 41:31.2 from all sorts of different environments 41:33.1 and all sorts of parts of the world 41:35.2 through the hard work of a large number of 41:39.0 ecologists and evolutionary biologists 41:41.2 who'd isolated these strains from the wild, 41:45.1 taking great care to not manipulate them in the laboratory, 41:48.1 but just get them put away right away. 41:51.2 So, we took these strains and we tested them 41:55.2 for changes in growth phenotype. 41:58.1 And so these are, 42:00.1 I'm keeping this just pretty simple, here, 42:02.1 but we're growing the cells in this plate in one condition, 42:04.3 in one type of growth media, 42:06.1 and over there we're growing them in a different type of growth media. 42:09.3 And you can see that some of the wells are light 42:12.1 and some of the wells are dark. 42:13.1 The reason for that is we've got these plates sitting on a light box 42:15.3 and we're shining the light through it, 42:17.2 and so when the cells have grown, 42:20.0 the light doesn't come through, 42:22.2 the media is turbid, 42:25.0 and the light shines through very, very nicely 42:28.0 when the cells are not growing. 42:29.2 And you can see... 42:31.2 these strains are arrayed in both plates in exactly the same way, 42:34.2 and you can see that the strains... 42:38.1 some strains can grow in one media, some strains can grow in the other, 42:41.2 some strains can't grow in either, 42:42.3 some strains can grow in both. 42:46.0 Now look at these strains, here. 42:49.2 These are 76 very different strains that we tested. 42:52.1 Now, we do something to inhibit Hsp90. 42:57.2 We add a low level of that Hsp90 inhibitor, 43:00.1 that doesn't affect the growth of most strains under most conditions, 43:04.2 but... and certainly not under conditions of rich media. 43:09.0 But in these different conditions, 43:11.0 we've used different carbon sources, 43:12.3 different nitrogen sources, 43:15.1 all sorts of different conditions, 43:17.2 high pH, low pH... 43:19.2 what we found was that 43:22.2 when we duplicated the growth curves 43:25.2 under exactly the same strains, 43:28.2 exactly the same media, 43:30.1 but in this case we just have a little bit of Hsp90 inhibitor 43:32.3 in this plate, 43:35.2 you can see that strains that 43:37.3 were unable to grow before 43:40.1 had genetic variation in them 43:43.0 that allowed them... a new trait to appear... 43:45.2 allowed them to acquire a new capacity to grow 43:47.2 when the Hsp90 buffer was inhibited. 43:49.3 And equally, over here, 43:52.0 you can see a whole bunch of traits that disappear. 43:54.1 Those cells could grow perfectly well under that condition 43:58.1 until the Hsp90 buffer was inhibited. 44:00.2 So, these are examples in wild yeast strains 44:04.3 where Hsp90 is acting very broadly 44:08.0 as a potentiator of some genetic information 44:12.1 and as a capacitor of other genetic variation. 44:14.1 A potentiator that allows new traits 44:17.2 that disappear when stress occurs, 44:19.1 or as a capacitor which kind of keeps genetic variation hidden 44:23.1 and then causes new traits when the Hsp90 buffer is brought down. 44:30.2 Now, most of these very same traits 44:33.2 occurred in the absence of Hsp90 inhibition, 44:36.2 when the strains were simply grown at higher temperatures, 44:39.1 so, again a very natural environmental stress 44:42.2 that creates a protein folding stress, 44:44.2 uses up a variety... 44:46.2 it creates a variety of protein folding stresses, 44:48.2 but most particularly effects the Hsp90 buffer, 44:51.0 this protein which is normally there to serve in excess, 44:54.3 and depletes it. 44:56.1 And the reason why we think Hsp90 44:59.1 is particularly important, again, 45:00.2 in this particular case, 45:02.2 is that we're seeing the same kinds of traits 45:04.2 with this environmental stress 45:06.2 as we see with this very selective inhibition of Hsp90. 45:10.3 So, yeast then allows us to 45:13.3 sort of get some molecular understanding 45:16.0 of how these traits occur. 45:17.2 So, we took two strains of yeast 45:21.1 that had been isolated from very different environments, 45:24.1 from grapes over here and from figs over there, 45:26.3 and I've just kept this diagram very, very simple 45:29.2 and pretended that the chromosomes of one strain are blue 45:33.1 and the chromosomes of the other strain are red. 45:35.0 We cross the strains, 45:37.0 and then in the next generation what happens 45:39.1 is there's genetic reassortment of those genes 45:42.1 through crossovers and recombinations 45:44.2 and just assortment of the chromosomes, 45:46.3 and you can see that the genetic variation 45:49.1 has gotten mixed up in the progeny. 45:50.2 And so what we could do, 45:52.2 we could look at hundreds of these progeny 45:55.0 and look at progeny that shared 45:58.0 the same genetic variation. 45:59.1 So, for example, this one 46:01.2 has this little red region of the chromosome, 46:04.2 and that one over there also has in common 46:07.2 the little red region of the chromosome. 46:09.0 And so we... 46:11.1 that allowed us to sort of map which regions of chromosomes, 46:14.1 which regions of the genome, 46:16.1 were responsible for those traits. 46:18.0 We actually mapped 400 traits. 46:21.3 These are called quantitative trait loci 46:24.1 because they can be measured in a quantitative way 46:26.1 in how they affected growth. 46:28.2 And we looked at traits across 100 different environmental conditions. 46:34.3 And we found that fully 46:38.1 50 of them disappeared when Hsp90 was inhibited, 46:42.2 that buffer was reduced, 46:44.2 and 50 new traits appeared 46:47.1 when the buffer was reduced. 46:50.1 So, that's 100 of the traits that we had mapped 46:53.2 depended upon, 46:55.1 the manifestation of those traits depended upon, 46:57.2 this Hsp90 protein folding buffer. 47:01.1 And so we were also able to ask, then, 47:03.3 with that experiment, 47:05.1 whether we could see evidence that this process 47:07.3 had been really operating 47:09.2 and had left an imprint on the genome sequences 47:11.3 of these strains that exist today. 47:14.2 So, what we did is to simply, 47:17.1 after having measured their growth rates 47:19.2 under 100 different conditions, 47:21.1 we simply lined up the strains according to... 47:29.2 and these strains... 47:30.2 sorry, I should have mentioned 47:32.2 that these strains were sequenced by other laboratories, 47:34.2 so we had the full genome sequence of these strains. 47:37.1 And when you lined up the strains 47:39.2 according to their genome sequence, 47:41.1 you could see that the wine strains were quite closely related to each other, 47:43.3 the clinical strains that we were looking at here 47:46.1 were more closely related to each other, 47:47.3 and that sake strain over there from Japan 47:50.2 was really quite distant. 47:52.2 And when we clustered by phenotype, 47:54.2 that is, arranged them next to each other 47:56.2 depending on how similar they were 47:59.1 under those 100 different growth conditions, 48:02.1 surprisingly it didn't line up with the genome sequence very well, 48:07.2 until we stressed them by reducing the Hsp90 buffer, 48:15.0 or by simply exposing them to high temperatures. 48:19.2 So what that really means is that 48:21.1 there's a vast amount of genetic variation out there 48:24.1 that, you know, 48:25.3 would appear to be neutral 48:27.2 and just random and chaotic 48:29.2 and have nothing to do with the phenotypes of the strains, 48:32.2 until you stress the organism. 48:34.3 So, when you're growing the organism in the laboratory, 48:37.1 you're missing a lot of the really important genetic variation 48:40.1 that evolution has been acting on in those strains, 48:44.0 and that's why the genotype/phenotype relationships 48:46.1 match up much better when you inhibit Hsp90. 48:50.2 So, can we see any evidence of this operating 48:55.1 in fish or some higher organism? 48:59.0 The reason why we've turned to fish 49:00.3 was because it's been a very, very special 49:04.2 evolutionary system, 49:07.0 and we had a wonderful, wonderful collaboration 49:09.2 with Nick Rohner and Cliff Tabin on this. 49:13.1 And really the work I'm going to be showing you in the next few slides 49:18.2 was all their work, 49:20.1 we just really helped as cheerleaders 49:22.1 to get them going on the experiment 49:24.0 and to really provide some advice 49:27.2 and some hints about how to do things. 49:30.1 So, what they had is this very special system of evolution 49:33.1 in which closely related fish 49:37.2 that are growing out in open waters 49:42.1 have repeatedly been sequestered into caves, 49:45.0 and in those caves they evolve new traits. 49:47.0 They, for example, lose their eyes, 49:50.1 and it's thought to be a selective advantage to lose your eyes, 49:53.2 because if your eyes aren't doing you any good 49:55.3 and you're bumping into things in the dark, 49:58.2 they just provide a vulnerability for infection, etc. 50:02.2 And they require a lot of energy to maintain eyes. 50:07.1 So, these are these surface fish and cave fish. 50:12.0 And what we did was simply to 50:16.2 use those inhibitors I told you about a little while ago 50:19.0 to bring down the Hsp90 buffer 50:21.1 and asked whether 50:23.1 -- these are the surface fish -- 50:25.1 would we see hidden genetic variation in them 50:28.1 that would cause the distribution of different types of eye sizes. 50:31.3 And sure enough, and after they were treated with Hsp90 inhibitor, 50:34.2 sometimes the eyes got bigger 50:36.3 and sometimes the eyes got smaller. 50:40.3 What could have been the environmental stress 50:43.2 that might be operating on these cave fish? 50:48.2 If you consider their environment, 50:50.2 it turns out that they're growing at quite similar temperatures, 50:53.3 but in this case they're exposed to different types of environmental stresses. 50:57.3 They're growing under very different pHs 51:01.1 with different amounts of dissolved oxygen, 51:03.0 and with very different amounts of dissolved solvents in the water 51:07.0 that they're living in. 51:09.1 So, we asked whether those kinds of stress conditions, 51:14.0 those environmental stresses, 51:16.0 could reveal the same kinds of variation 51:18.2 in eye morphology, 51:20.1 and we got very similar results. 51:24.0 And then we had to ask, of course, 51:27.0 whether that was really due to just 51:29.2 randomized effects on development, 51:31.1 or whether it was due to underlying genetic variation 51:35.2 that could be enriched in subsequent generations 51:38.2 to start driving these fish towards a new phenotype. 51:42.1 And so when Nick crossed fish together 51:47.3 that had the smaller eyes after the Hsp90 buffer 51:53.2 was compromised, 51:54.3 what he found in the next generation was that 51:57.3 most of the fish had smaller eye size, 52:01.1 even when the Hsp90 buffer was not compromised. 52:04.1 So, it was in fact 52:07.0 due to underlying genetic variation 52:10.0 and that genetic variation that 52:12.2 was enriched in subsequent generations 52:15.2 could manifest as a smaller eye now, 52:19.0 without any need for Hsp90 inhibition. 52:21.3 And in fact there was evidence that 52:24.2 this has been happening with this cave fish evolution, 52:27.2 because when we looked at the ability of these Hsp90 inhibitors 52:31.0 to cause variation in eye size in the cave fish, 52:34.0 it did cause variation in eye size, 52:36.1 but not towards larger eyes; 52:38.1 they lost that variation under the selective pressures 52:41.2 of living in the cave. 52:43.1 So that was an example of 52:47.2 a really wonderful collaboration 52:49.3 with some really extraordinary scientists 52:52.0 that were working on a model system 52:53.3 that we couldn't begin to approach ourselves, 52:55.2 and we're really, really excited about their willingness 52:58.1 to try these things out. 53:01.2 So, I just want to present 53:05.1 maybe one more aspect of Hsp90 and evolution 53:08.1 in human beings, 53:09.3 and this is just going to be one quick slide. 53:12.0 This is a map of the human kinases 53:15.1 and how they're related to each other. 53:16.2 The kinases, as I mentioned earlier, 53:18.2 are these signal transducers that do all kinds of... 53:24.2 regulate all kinds of processes in the cell. 53:28.1 And we have systematically quantified 53:31.2 the dependence of these different kinases on Hsp90, 53:35.1 and what we have found is that 53:37.1 some of those kinases 53:39.2 don't depend on Hsp90 at all, 53:41.2 others depend on Hsp90 moderately, 53:44.2 and some of them are really strongly dependent upon Hsp90. 53:48.2 So, you can see in very closely related kinases 53:53.1 that they're acquiring mutations 53:57.1 that contribute to their new functions, we think, 53:59.1 but those mutations also cause them, 54:01.3 like they did with the oncogenic mutations, 54:04.0 to have a very differing level of dependence upon Hsp90. 54:07.1 So this is kind of an imprint of that Hsp90 54:11.1 influencing the evolution of kinases 54:13.2 in the human genome. 54:16.1 So, Hsp90 transforms the adaptive value 54:20.1 of large amounts of standing genetic variation. 54:22.2 It affects polymorphisms that are located 54:25.2 throughout the genome 54:27.2 and it even actually affects non-coding polymorphisms, 54:30.2 and you can look at Dan Jarosz's paper for that. 54:32.2 But what it does is it does it in a combinatorial way, 54:36.0 because this protein folding buffer stress 54:38.0 means that lots of different mutations 54:40.2 all of a sudden 54:42.3 can exert their effects. 54:45.2 So it can create quite complicated phenotypes 54:48.1 in a single step. 54:50.1 And the traits can be assimilated by selection, 54:52.2 so the, 54:56.1 like the drug resistance case, 54:58.2 new mutations cause those resistance phenotypes 55:01.2 that initially depended upon Hsp90 55:04.2 to be more robust, 55:07.0 and to be present even when Hsp90, the inhibitor, 55:09.3 was there. 55:11.1 And with the fruit flies, 55:13.0 the selection of pre-existing genetic variation, 55:14.2 by reassortment in subsequent generations, 55:16.2 could also fix the traits. 55:19.1 Simple environmental stresses 55:22.2 exert very similar effects on genetic variation. 55:27.1 And Hsp90 has sculpted, then, 55:29.2 the standing genetic variation 55:32.2 that exists in genomes today, quite broadly. 55:36.0 And understanding this aspect of human evolution 55:39.0 matters importantly to human health. 55:42.2 I told you about just two examples of that. 55:44.2 I've told you about drug resistance 55:46.2 and I've told you about cancers, 55:48.3 but there are many other examples as well. 55:52.1 And there are many other features of this Hsp90 buffer 55:56.0 that I haven't told you about 55:59.2 that... the work of other laboratories 56:01.2 that have shown other ways in which Hsp90 56:03.2 can influence the appearance of new traits. 56:07.2 So, Hsp90 really provides a very plausible mechanism 56:10.1 by which traits that are initially caused by environmental change 56:14.2 can be assimilated 56:17.0 and lead to new robust evolutionary traits. 56:19.1 And, you know, one reason 56:22.0 I've shown this picture of 56:24.2 this same exact place at two different times of year 56:27.1 is just to remind you that this is really... 56:30.2 these changes in environment 56:33.0 are something that all organisms have been facing 56:34.3 since the dawn of time, 56:36.3 so it's not really all that surprising that Hsp90, 56:40.2 and many other mechanisms, 56:43.2 would be tied to the functionality of proteins, 56:46.3 tied to the effects of environments, 56:50.0 would be able to actually lead to new traits. 56:55.1 And what's interesting about it is that this driving force 56:58.3 for the evolution of new traits 57:00.2 is not linked to the mutations themselves 57:02.0 that cause the new traits, 57:04.1 and that's one of the reasons why the system can survive. 57:06.3 We're not saying that the system evolved in the first place 57:09.3 to create evolution; 57:12.0 we're saying that these proteins evolved 57:13.3 to help other proteins fold, 57:15.3 but in that process, 57:17.1 the very fact that they help other proteins fold 57:18.3 and that it exists in excess 57:22.1 and can be used up by stress, 57:24.2 is what creates... the consequence 57:28.0 is that it also influences evolutionary processes. 57:30.3 So, with regard to Lamarck, 57:33.1 who's been excoriated in the literature and in our textbooks 57:37.3 for a couple of centuries now, 57:40.3 I think the inheritance of environmentally acquired traits 57:44.0 is a perfectly reasonable thing to contemplate 57:45.3 and has happened, in fact, 57:48.0 in evolution again and again, 57:49.3 and it's time to get him back some of his dignity. 57:53.0 And I want to finish by doing just one thing 57:56.0 and that is acknowledging the key players 57:59.1 who did all of this work. 58:01.1 I've been running the laboratory, 58:05.1 but I haven't been doing experiments myself for quite a few years. 58:07.2 I have, however, been fortunate 58:10.2 to be associated with these really remarkable people. 58:13.1 Many, many other remarkable young people in my laboratory 58:16.1 have contributed as well, 58:18.2 but the particular experiments that I showed you 58:21.1 were performed by these people, 58:23.1 and I want to give them all the credit. 58:25.1 So, thank you for listening.

Part 4: Prions: Protein Elements of Genetic Diversity

00:07.1 Hi, I'm Susan Lindquist. 00:09.1 I'm at the Whitehead Institute at MIT 00:11.2 and a member of the Howard Hughes Medical Institute. 00:14.1 I'm here to tell you about protein folding 00:16.3 as a powerful driver of evolutionary novelty. 00:20.0 Last time, I talked to you about Hsp90 00:23.1 and the ways in which it can influence protein folding 00:26.1 and the manifestation of genetic variation 00:28.1 in very powerful ways. 00:30.1 Today, I'm going to tell you about 00:32.1 a very different way in which protein folding 00:34.1 can influence the manifestation of genetic variation 00:36.1 and lead to the appearance of all kinds of new traits. 00:40.1 And that's the prions. 00:42.3 So, it's an interesting story 00:45.0 that starts in a place in New Guinea 00:49.1 and will move on to Cambridge, Massachusetts. 00:52.1 So, the protein folding problem, 00:55.2 which drives all of this that I'm going to be talking to you about, 00:58.1 is simply that proteins start out as 01:01.2 long linear strings of amino acids 01:03.1 and they have to fold into very complicated shapes like this 01:06.1 in order to function, 01:07.2 and they do that in a crazy environment. 01:09.2 They do that in this really crowded environment of the cell 01:12.2 where proteins are jostling around and bumping into each other all the time. 01:16.1 The story on prions, as I mentioned, 01:18.1 starts out in New Guinea, 01:21.1 and there was a tribe where 01:25.1 a very large number of people were dying from 01:26.3 a very bizarre, horrible neurodegenerative disease, 01:29.3 and Carleton Gajdusek found out that it was 01:33.1 in fact due to an infectious agent. 01:36.3 And Stan Prusiner 01:40.0 -- and many other investigators have contributed to this, 01:42.2 I should say, 01:44.1 but these guys were very, very, very important, 01:46.2 pivotal in the field -- 01:48.2 found out that this disease 01:53.1 was caused by protein folding. 01:55.1 Now, the protein folding problem 01:57.2 creating an infectious element 01:59.1 was really quite an amazing thing, 02:00.3 and it meant that proteins, when they change their folds, 02:03.1 can have genetic manifestations... 02:06.3 disease agents are usually 02:09.0 agents that carry along DNA with them, 02:11.1 so it was quite a revelation. 02:13.1 But what I want to talk to you about 02:16.1 is thinking about prions like this. 02:20.1 Because the story of prions as disease-causing agents 02:23.3 is so extraordinary and wonderful and amazing. 02:27.1 It's kind of dominated, and quite naturally so, 02:31.0 the concept of prion biology. 02:34.0 But what I think is that we have to think about prions 02:37.2 as this creature, 02:39.1 and it's time to get rid of some of the baggage, 02:42.1 because it's my belief that prions actually are 02:45.1 amazing protein-based genetic elements 02:48.1 that can do all kinds of really wonderful things 02:50.3 in biological systems. 02:52.2 And I think also that we're only 02:55.0 at the very tip of the iceberg 02:57.1 in revealing this. 02:59.2 So, here's some of the great things about prions 03:01.2 that I want to tell you about. 03:04.2 Prions form a mechanism for the inheritance 03:07.2 of a protein-based trait. 03:09.2 We've found more than 50 of them in yeast, 03:12.0 we and other people have reported on these. 03:14.1 25 of them are as yet unpublished, 03:15.3 but there's just a lot of them. 03:19.0 They cause dozens, 03:22.2 a bewildering variety of all sorts of new traits. 03:25.0 They allow organisms, the yeast organism, at least, that we've been looking at, 03:30.3 to survive in fluctuating environments. 03:33.2 They provide a fast route to the 03:36.1 evolution of complexity 03:38.2 and they're reversible traits, 03:41.3 so the organism can acquire these traits 03:44.0 and they can lose them. 03:46.2 They form a very sophisticated 03:50.1 bet-hedging strategy for survival. 03:53.1 And they form a system of biological memory, 03:56.1 in which the change in protein folding 03:58.2 self-perpetuates 04:01.1 and that self-perpetuating change in protein folding 04:04.1 changes the function of the protein, 04:06.1 and that can both create new phenotypes 04:09.3 and it actually serves as a kind of a biological memory, 04:12.1 when you think about it, 04:14.1 and there's evidence now 04:16.1 that this is really occurring at the ends of synapses 04:18.2 in our brain 04:20.1 and helping to maintain long-term 04:22.2 synaptic connections. 04:25.1 Also, we have found, in my lab, 04:28.0 evidence that prions can enable 04:32.1 life in complex biological systems and communities, 04:35.2 creating different ways for organisms to relate to each other. 04:39.1 So I'm going to try to give you 04:41.3 an overview of this very complex and rich 04:44.1 and wonderful subject. 04:46.3 And I'm going to end with prions forming, really, 04:52.1 the ultimate example of Lamarckian evolution, 04:55.1 in which organisms can acquire a heritable new trait 05:00.1 that they pass on from generation to generation 05:03.1 by being exposed to a new environment. 05:09.2 So, the prion story 05:12.1 starts with these two strains of yeast, 05:19.2 red versus white, 05:21.2 and it turns out that these organisms 05:29.0 have a very odd genetic behavior. 05:31.1 Brian Cox did some wonderful work on this 05:34.3 many years ago, 05:36.1 and a lot of people thought, well, this is really kind of a strange thing, 05:38.2 why is he working on this?... 05:40.2 but his early work really laid the foundation 05:42.3 for all of this, 05:44.3 everything I'm going to be talking to you about today. 05:46.2 What he found was the red strain 05:49.3 was really very stable and you could streak it out 05:52.2 and it would give rise to more red colonies, 05:54.1 and every once in a while, they would turn white. 05:56.2 And the white strain you could streak out 05:58.2 and they were very stable, 06:00.2 a very heritable trait, 06:02.3 and every once in a while, they would turn red, 06:04.3 and back and worth. 06:06.3 So it was metastable inheritance. 06:09.1 What Brian also found out is that 06:12.2 when you mated the red cells to the white cells 06:14.2 and then you sporulated and got out the genetic progeny, 06:19.0 you would normally expect, 06:21.0 if those traits were due to changes in the DNA, 06:24.0 if the difference between the red and white cells 06:26.1 was due to changes in the DNA, 06:27.3 that some half of the progeny would now be red 06:30.1 and half of the progeny would now be white, 06:31.3 as those pieces of DNA reassorted in subsequent generations. 06:36.0 And they weren't. 06:37.2 All the cells, all the progeny were white. 06:39.1 So, an odd inheritance. 06:41.3 Brian also realized that this change in color 06:46.3 was due to a change in translation, 06:49.1 the way in which messenger RNAs are decoded into proteins, 06:52.2 that there was a translation termination defect, 06:55.1 and the cells switched from red to white 06:58.1 because of a change in ribosome readthrough 07:01.3 of stop codons. 07:04.2 And that was linked to this protein here, 07:07.1 in this simple cartoon. 07:09.3 It's a protein that's involved in translation termination, 07:13.2 and it's only this portion of the protein over here 07:16.1 that's actually required for translation termination activity. 07:20.2 It's a very important factor. 07:22.1 It tells ribosomes to stop when they hit a stop codon. 07:26.1 Attached to it are these two weird domains, 07:28.3 called the N-terminal domain 07:30.1 and the middle domain, 07:32.1 that just have a very unusual type of 07:35.1 amino acid composition. 07:36.3 And it was discovered that those properties of inheritance 07:39.3 really depended upon that... 07:43.1 that is, those odd properties of inheritance 07:44.3 depended upon that region of the protein. 07:47.2 Now, Yury Chernoff comes into this story 07:51.1 because he was searching for... 07:54.1 what could govern this odd pattern of genetic behavior? 07:58.1 It's not like the genetics that we normally think about 08:03.0 as being driven by changes in DNA. 08:05.2 And he was working with Sue Liebman's laboratory, 08:08.1 and he did a screen for 08:11.2 factors in the genome of yeast 08:13.2 that could alter or influence this inheritance pattern. 08:19.1 And what he came up with was Hsp104. 08:23.2 But what the heck did that mean? 08:25.2 Well, at that point, I got a phone call from Yury, 08:29.2 because he knew that I had been working on Hsp104. 08:34.3 My laboratory had shown that it saves cells 08:37.2 from high-temperature death, 08:39.1 and he was wondering if I knew any aspect 08:43.3 of how it worked. 08:45.2 And I did, I knew how Hsp104 worked, 08:51.0 but nobody else did, 08:52.3 and the reason for that was because 08:54.2 we couldn't get our paper published. 08:56.0 Hsp104 did something unusual 08:58.0 and it was very hard for people 09:01.2 to accept what it did. 09:03.2 So, what does Hsp104 do? 09:05.1 And by the way, these are many, many, many people 09:08.1 who contributed to this story 09:10.2 of trying to figure out these different parts of this translation termination factor 09:14.2 and which ones contributed to the genetic behavior. 09:18.1 So, what does Hsp104 do? 09:22.2 Hsp104 plays a major role 09:25.1 in something called induced thermotolerance. 09:27.2 I talked about this briefly in one of my earlier lectures. 09:30.2 When organisms are exposed to mild heat temperatures, 09:34.1 they make new proteins 09:36.0 called heat shock proteins, 09:37.2 and those proteins help to save them 09:39.3 from the death caused by protein misfolding at high temperatures. 09:43.2 Here's an experiment in which 09:46.1 the cells have been exposed to the same heat treatments, 09:48.3 but in one case the cells have Hsp104 09:51.3 and in the other case the cells do not have Hsp104. 09:54.2 It makes a big difference to their ability to survive, 09:58.0 and the way it makes a difference in their ability to survive 10:01.2 is that it takes apart protein aggregates. 10:03.1 So, what are protein aggregates? 10:08.1 These are well-folded proteins. 10:10.3 You've all seen the effects of heat 10:12.3 on these well-folded proteins. 10:14.3 It causes proteins to aggregate. 10:16.2 Now, Hsp104 I don't want to say 10:20.2 can unfry that egg, 10:22.1 but just a little bit of that kind of aggregation 10:26.2 occurs in cells in response to stresses, 10:28.3 and that can kill them. 10:31.0 Hsp104 saves them 10:33.2 by disaggregating it. 10:35.2 So, that led us to think, well maybe, 10:38.1 how could it be involved in inheritance of this trait? 10:40.2 Maybe the inheritance of that trait 10:43.2 depended in some way upon 10:46.1 protein aggregation phenomena? 10:48.1 So we looked at the protein that had been 10:52.1 tied to that trait, 10:53.3 that translation termination factor, 10:55.2 and asked whether it existed in 10:58.1 a different conformational state 11:00.1 in the red cells or the white cells. 11:01.3 And sure enough, in the red cells 11:04.2 the protein was soluble and functional, 11:07.1 and in the white cells the protein was 11:11.0 tied up in little aggregates. 11:12.0 And you can see by the way... 11:13.3 these little aggregates, 11:15.2 they're actually being inherited, 11:18.1 they're being passed from the mother cell into the daughter cell. 11:22.3 Now, about the time when we were in the midst of these experiments, 11:26.1 and we hadn't yet published anything 11:29.0 and we hadn't yet been able to publish 11:33.1 even our story on Hsp104, 11:34.2 although we did wind up getting a really great paper 11:36.2 and the suggestions of the reviewers, in the long run, 11:39.1 really did make a better paper... 11:42.2 while we were in the midst of this, 11:44.1 along came a paper by Reed Wickner, 11:47.2 which was a very, very clever interpretation 11:52.0 of some genetic experiments 11:54.0 on another factor that was inherited 11:56.0 in a very bizarre way, 11:57.2 inherited and had the same kind of genetic properties 12:00.0 that the PSI element had, 12:02.2 that that red/white element had. 12:05.2 This was called URE3, 12:08.1 and he suggested that the way in which this was inherited 12:12.3 was that it was due to some kind of 12:14.3 a prion-like phenomenon, 12:16.2 a self-perpetuating change in protein function. 12:21.3 Not knowing whether it was an aggregate, 12:23.3 or whether it was a change in the activity of the protein, 12:27.0 or what was going on, 12:28.3 but he also suggested that this might apply to... 12:32.0 this prion-like mechanism might apply 12:35.3 to that inheritance of that red/white trait. 12:39.3 So, as I say, it could have been anything, 12:42.3 it could have been an enzyme that catalyzed its own modification, 12:46.1 it could have been lots of different things, 12:48.3 but because we knew that it was controlled by Hsp104, 12:51.3 which we knew was involved in protein aggregation, 12:54.2 this paper really made a tremendous amount of sense. 12:58.3 And so we decided to look at this 13:02.2 in even greater detail, 13:05.1 and we found that, in fact, the aggregation state of that protein, 13:09.0 Sup35, 13:10.2 that translation termination factor, 13:14.2 was not just a generalized aggregate, 13:18.1 but a very special kind of aggregate, 13:20.2 a self-templating amyloid aggregate. 13:24.1 So, on the left there you see 13:27.1 the protein fibers of Sup35 that we found, 13:31.2 after a great deal of effort. 13:34.2 Aggregated proteins are a lot of work to work with, 13:36.2 but we eventually looked at the aggregates 13:39.1 under the electron microscope 13:41.1 and, lo and behold, they're not just a mess of aggregates, 13:43.0 they were very highly organized amyloid filaments. 13:46.2 And they were organized in a very interesting way, 13:48.2 because the central spine of the filament 13:51.1 was that region of the protein 13:53.2 that had previously been shown to be essential 13:56.3 for the inheritance of the white trait, 14:01.0 and the functional part of the protein, 14:03.1 that's normally functioning in translation, 14:04.3 is stuck out on the outside, 14:06.2 so when this protein gets assembled into this amyloid fiber, 14:10.2 it can no longer get to the ribosome 14:12.2 and is no longer functioning. 14:15.2 And we were then able to actually 14:18.3 monitor the assembly kinetics of this protein, 14:21.2 and we found that the protein 14:25.1 initially assembles in a test tube 14:28.1 very, very inefficiently 14:30.1 and very, very slowly. 14:31.2 It took hours and hours and hours for the protein to assemble... 14:34.2 but, the key thing that I think explains 14:38.1 the ability of this protein to serve as an element of genetic inheritance 14:41.3 is that if you take a very small amount 14:46.2 of this preassembled protein 14:49.1 and add it to the start of a new assembly reaction, 14:51.3 this one right here, 14:53.1 what you see is that... 14:56.2 boom... 14:57.3 the preassembled fibers have the capacity 15:00.1 to completely, very, very rapidly 15:03.1 convert all the other protein to that same state. 15:06.3 So, now you can begin to see 15:11.1 how this could explain the inheritance of this trait, 15:14.1 because if you have a soluble protein that's functional and the cells are red, 15:20.1 and an insoluble protein that's not functional, 15:22.1 so that the cells are white. 15:24.2 You mate those cells together 15:26.2 and then you sporulate and segregate the genomes, 15:29.2 well, the protein has served as a template 15:32.0 to change the protein in the other cell type, 15:35.0 and so that when the cells sporulate, 15:38.0 all of the progeny are going to be white, 15:40.1 as Brian Cox had shown. 15:42.1 And it doesn't segregate with the DNA, 15:44.1 it doesn't matter which cells get which chromosome, 15:47.3 because the trait is not based upon the inheritance 15:50.1 of a DNA change, 15:51.3 it's based upon the inheritance of a protein 15:54.2 with an altered conformational state. 15:57.1 And how Hsp104 figures in this 16:00.1 is that Hsp104 controls that amyloid state. 16:05.2 I told you that it saves cells from heat shock 16:07.2 by disaggregating proteins; 16:09.2 it also can disaggregate those amyloids. 16:12.1 So... and in fact it can cut them 16:14.1 into little bits and pieces. 16:16.0 So, here are fibers of that protein, 16:19.1 amyloid fibers, 16:21.1 very tough, difficult to dissolve biochemically 16:25.3 #NAME? 16:27.3 to get them to dissolve -- 16:29.1 but here's what Hsp104 does. 16:31.0 It chops them into little tiny pieces, 16:33.1 and that allows them to be inherited, 16:36.2 and to form the template 16:39.1 that goes into the daughter cell 16:41.1 and allows the daughter cell's proteins to change. 16:45.1 So, the whole thing, putting that together, then, 16:47.2 looks like this. 16:49.1 You have red cells 16:51.0 that are carrying a particular gene, 16:53.1 and when ribosomes do what they're supposed to do, 16:55.3 the translation termination factor is in its soluble state, 16:59.2 it tells the ribosomes to stop 17:02.0 when the ribosomes see the stop codon. 17:06.0 But that protein can assemble 17:08.2 into this self-perpetuating amyloid, 17:10.2 and when it does there's not very much of the translation termination factor around, 17:14.2 and so quite a few of the ribosomes 17:16.2 wind up reading through that stop codon, ignoring it. 17:20.1 That changes the cells 17:22.2 from having a red pigment to having a white pigment. 17:27.1 Now, the cells turn white, 17:32.3 and the next key, the inheritance of this, 17:36.2 is that the self-templating amyloid protein 17:40.3 is actually cut by Hsp104 17:44.3 to allow its orderly segregation 17:47.3 into daughter cells for inheritance. 17:50.2 And in fact with Helen Saibil, 17:54.2 we overexpressed this protein to the point 17:57.0 where it made massive assemblies, 17:59.1 amyloid assemblies in the cell, 18:00.3 and used some very, very sophisticated imaging techniques, 18:06.0 like EM tomography, 18:07.3 this was all done by Helen by the way, 18:10.0 to suggest that, 18:12.2 just like polytene chromosomes 18:14.3 #NAME? 18:16.3 gave us our first view of 18:19.2 the organization of DNA in chromosomes -- 18:24.0 we think these larger assemblies 18:26.1 are giving us the first view of the way 18:28.2 in which they, 18:30.0 actually a rather sophisticated mitotic apparatus 18:31.2 that breaks these fibers apart, is working. 18:34.2 But in any case, Hsp104 is key to it, 18:37.3 but it's not the only key to it. 18:39.1 There are multiple other proteins that are involved 18:42.1 in helping these amyloid fibers 18:45.1 to be partitioned to daughter cells 18:47.2 in a very orderly way, 18:49.2 guaranteeing the inheritance of the trait 18:52.1 that's caused when this protein changes 18:55.1 its conformational state. 19:00.1 So this provides a completely coherent biochemical explanation 19:04.2 for this phenomenon. 19:06.1 Is it really responsible, 19:08.0 is it the only thing that's going on? 19:10.1 Well, we did a lot of things. 19:13.0 We had mutations in the prion 19:15.1 that didn't assemble very well, 19:18.2 and they could not inherit the trait. 19:20.1 And we had ones that caused it to assemble more 19:22.1 and they were more likely to inherit the trait, 19:23.2 we did all sorts of things, 19:25.0 but let me just tell you about one line of evidence 19:28.0 that was particularly fun. 19:29.2 We figured that, well, 19:32.0 if this really was responsible for that new trait, 19:36.0 this protein assembly process, 19:38.0 then we should be able to use that knowledge 19:40.2 to create a new prion, 19:42.2 all of our own. 19:44.1 And so what we did was to take 19:47.2 the Sup35 translation termination factor 19:50.1 and in the genome we deleted the prion element 19:53.0 from that strain, 19:54.1 because we didn't want it to interfere with our new prions 19:56.2 that we were going to be making. 19:58.1 And then we took glucocorticoid receptor, 20:00.0 which is a steroid hormone receptor from rat, 20:02.3 and asked whether or not 20:05.2 we could monitor its activity in a yeast cell, 20:08.0 and see a change in its activity state 20:10.2 when we fused it to that domain 20:13.3 which we now call the prion domain of this protein, 20:17.2 which is responsible for this bistable state. And sure enough... 20:22.2 we had a reporter in there that, 20:24.1 when the glucocorticoid receptor was working, 20:26.1 it would turn the cells the blue. 20:29.1 When the glucocorticoid receptor was assembled 20:32.1 into a self-perpetuating aggregated template, 20:34.2 the cells became white, 20:37.3 because it was no longer working 20:40.1 and, moreover, they passed that white state on to their progeny 20:42.2 in a very, very stable way. 20:44.2 So, they could either pass on the blue state 20:46.1 or they could pass on the white state. 20:48.0 So, the Weissman lab did another wonderful experiment. 20:50.0 They took the cell walls off of yeast cells 20:54.2 and they did a protein-only transformation, 20:57.2 assembling the protein into those fibers 21:00.0 that I told you that we made earlier, 21:02.2 they made them under a couple of different conditions, 21:04.2 and they used those fibers, 21:07.3 they stuck them into the yeast cells, 21:10.0 and they were able to show that the cells 21:13.1 turned from red to white 21:15.3 in a heritable way. 21:17.1 So, that protein-only transformation 21:20.2 really was another nail establishing the coffin, 21:25.2 establishing that this genetic trait 21:29.1 was due to the inheritance of a protein 21:31.3 with an altered conformation. 21:33.1 Now, here we have this prion protein. 21:37.1 As I mentioned, it's a translation termination factor, 21:40.0 a really important factor in the cell 21:42.1 that determines when ribosomes 21:44.3 will interpret stop codons properly, 21:48.0 and it's conserved in all eukaryotes, 21:50.1 and here we have this domain stuck onto the end of it 21:54.3 that has been conserved, by the way, 21:57.0 for 800 million years of evolution, 21:59.2 and its regulation by Hsp104 22:01.2 has been conserved for 800 million years of evolution. 22:06.3 And so the question becomes, 22:09.3 why? 22:11.1 Why in heaven's name 22:13.2 would cells allow this translation termination factor 22:18.3 to suddenly be sucked out of solution 22:21.1 so that ribosomes aren't performing properly? 22:25.0 And it occurred to us that 22:28.1 this had to have some meaning, 22:31.1 because otherwise, yeast can very, very rapidly evolve, 22:34.3 and they could have acquired mutations 22:37.2 in that prion domain 22:39.1 that would have still allowed the protein to have its translation termination function, 22:42.1 but not allowed it to be sucked up into these aggregates. 22:45.0 So, one thing that occurred to us was that 22:49.1 the readthrough of ribosomes 22:51.2 might be occurring not just on 22:54.2 that one messenger RNA that Brian Cox had first discovered, 22:58.2 but actually it might be happening on messenger RNAs 23:01.0 that were coming from all over the genome, 23:03.2 and in that case we might expect to see 23:06.1 some really new and interesting traits 23:08.2 if we started growing strains in different conditions, 23:10.3 not on rich media, 23:12.2 in the laboratory. 23:14.0 So, here's an example of a really wonderful trait 23:16.0 that's caused by the appearance of this same prion. 23:19.3 You can see that the colony morphology of these cells 23:24.1 has changed completely. 23:26.1 Cells that normally look like this 23:28.3 and create colonies like this 23:31.1 created very, very different types of colonies 23:33.1 that adhere to each other, 23:35.0 the cells adhere to each other, 23:36.1 they stick to each other, 23:37.2 they have different abilities to grow in different environments. 23:40.2 And here's an example of the fact that 23:44.1 these prions actually spontaneously appeared 23:47.0 every once in a while. 23:48.2 And we tried growing cells under 23:50.1 all sorts of different conditions, 23:52.1 kind of like the story I told you about Hsp90 23:54.1 a little while ago, 23:55.3 and we found that in different strains 23:58.3 we got completely different traits. 24:01.1 And that makes sense when you realize 24:03.3 that the regions that are downstream of stop codons 24:06.3 are not normally under much selective pressure, 24:08.2 they're free to vary quite a bit, 24:10.2 and so changes in those downstream sequences 24:14.1 might be expected to accumulate 24:17.2 over the course of evolution, 24:19.1 and then we the cell switches into the prion state 24:22.0 by, perhaps, sheer happenstance, 24:24.2 that will cause the creation of many new phenotypes. 24:30.1 So, you get lots of different traits in lots of different strains, 24:34.3 and the traits of different strains 24:38.0 were completely dependent upon the genotype of that strain. 24:41.0 So, what we think is that this prion 24:43.2 allows cells to sample genetic variation, 24:46.3 kind of at a genome-wide scale, 24:50.1 and it allows them to acquire 24:52.2 some really complicated traits 24:54.1 that it would be very hard for them to acquire 24:56.2 by individual mutation 24:58.3 for example, of a stop codon, 25:00.1 that would cause one individual messenger RNA 25:02.2 to be readthrough. 25:04.0 Rather, multiple messages being readthrough 25:05.3 at the same time 25:07.1 could create quite complicated traits. 25:11.2 So, if this mechanism has really existed, 25:15.2 this prion has existed, 25:17.2 in order to create evolutionary novelty 25:21.1 and the ability of the cells 25:23.2 to sometimes be able to exploit new environments, 25:25.2 then evolutionary biologists 25:29.3 and we ourselves realized 25:31.2 that if this was how this prion 25:35.1 was serving such a purpose in evolution, 25:37.2 then switching should increase with stress. 25:40.3 That is, it should be tuned such that 25:43.2 under stressful environments 25:45.1 the cells would be more likely to switch into the prion state, 25:47.1 because they would be more likely to need 25:49.3 novel new phenotypes under stress. 25:51.3 And so we asked whether or not it does, 25:54.1 and the answer to that was yes. 25:56.2 And then the question of course becomes how, 25:59.0 but for us there's a pretty simple, 26:01.2 logical explanation for that, 26:03.2 and that is because of how stress 26:07.2 influences the protein folding and protein homeostasis pathways 26:10.1 of the cell. 26:11.3 So, stress increases the likelihood 26:14.2 that proteins will misfold, 26:16.1 making it more likely that those prion domains 26:19.1 will now suddenly acquire 26:22.0 that aggregated amyloid state 26:24.0 and perpetuate that state to their daughter cells. 26:27.1 Stress also increases 26:33.1 the rate at which cells lose prions, 26:36.0 because the stresses cause 26:40.3 induction of chaperone proteins, 26:42.2 and induction of protein degradation mechanisms, 26:45.1 and all sorts of other things that influence protein homeostasis, 26:47.3 so these prions, 26:51.2 which normally appear only relatively rarely, 26:54.2 would, just because of the very nature 26:58.1 of protein homeostasis 27:00.0 and the way in which stress influences protein homeostasis, 27:02.0 be more likely to appear and disappear 27:04.0 under conditions when the cells might need new phenotypes. 27:07.1 So, here's the way we think it's working: 27:09.3 cells switch into the prion state just every once in a while, 27:13.3 and in fact with PSI, 27:16.1 the prion that I've just been talking to you about, 27:18.1 that translation termination factor, 27:19.3 that happens about one in a million cells. 27:22.1 And generally one would expect 27:24.2 that when the translation termination activity isn't working well 27:27.1 and ribosomes are reading through stop codons, 27:30.1 it's generally not going to create a good trait, 27:32.2 and so that one in a million cell might die 27:35.0 #NAME? 27:38.1 But you can imagine that, if the environment changes, 27:42.1 and that's what we found, 27:43.2 is that in different environments, 27:46.2 prions could sometimes give cells an ability to survive conditions 27:49.1 that they otherwise could not possibly survive, 27:52.2 then the cells would now be able to live in an environment 28:00.2 where they otherwise wouldn't be able to live. 28:02.1 They would proliferate, 28:04.1 the non-prion cells might disappear, 28:05.2 and this allows this genome, 28:07.2 the prion would allow this genome 28:09.1 to survive under conditions 28:11.1 when those cells would otherwise not be able to survive, 28:13.1 because the formation of that prion 28:15.2 has allowed all kinds of new genetic variation to be exploited, 28:18.2 some of which is beneficial. 28:20.2 Now, the cool thing is that 28:22.3 because this is all tied to protein homeostasis in the cell, 28:26.2 under conditions when the environments have changed 28:28.2 where cells are not doin' so well, 28:31.2 and protein homeostasis is not going so well, 28:33.2 they are more likely to switch. 28:37.1 And of course the return 28:39.2 is also going to be influenced by stress. 28:42.3 So, the protein homeostasis... 28:45.1 if cells change... 28:47.1 the prion may be really great here, 28:49.0 but if the environment changes, 28:50.3 under these new circumstances, 28:52.1 the prion might not be so good, 28:54.0 but under those stressful conditions, 28:55.3 they'd be more likely to try out 28:58.0 the loss of the prion, 29:00.2 because chaperones have been upregulated 29:03.1 and they've destabilized the whole system. 29:07.1 So, we decided to look for new prions. 29:14.2 How many new prions might there be? 29:18.0 We surveyed the yeast genome 29:19.2 and we found that there were 29:23.1 about 100 proteins that had domains on them 29:25.0 that looked a lot like the Sup35 domain, 29:27.3 and so we decided, 29:29.3 because we know so much about Sup35 29:31.2 and how it behaves and looks 29:33.2 when it changes into a prion state, 29:35.1 we borrowed that prion domain of Sup35, 29:37.2 we took it away, 29:40.3 and we added the prion domain 29:43.1 of these other potential new prions. 29:46.1 And sure enough, 29:48.2 when we tested them out, 29:50.1 many, many different protein domains 29:52.2 had the ability to switch cells from red to white 29:54.3 in a very heritable way. 29:57.1 Once the cells switched from red to white, 29:59.2 they could be struck out 30:02.2 and maintain that characteristic 30:05.0 for generation after generation, 30:06.2 and in every case that depended upon 30:09.3 the formation of prion amyloids, 30:12.1 and, in fact, it depended upon Hsp104. 30:16.1 So, we also went back, 30:18.1 not just to the prion domain that we were testing, 30:21.2 but we went back to the endogenous protein 30:24.1 and asked whether those proteins could switch states. 30:26.3 We couldn't handle all of them, 30:29.1 but we looked at several of them 30:31.2 and they created some really interesting beneficial new traits, 30:33.1 and intriguingly many of those proteins 30:36.1 are RNA binding proteins or DNA binding proteins, 30:41.0 so they sit in the middle of regulatory networks 30:42.3 in such a way that 30:44.3 they're really primed to change 30:47.1 the way information is being decoded 30:49.0 and really create some really complex, novel, new traits. 30:51.1 Here's just one example. 30:52.3 So, this is the prion known as MOT3, 30:56.2 and that's the prion- cell 30:59.0 and that's the prion+ cells 31:01.1 growing in rich media. 31:03.1 These are a variety of cells that have the prion. 31:05.2 And now we wash away that media... 31:08.2 sorry, these cells from that media, 31:12.0 and we look to see 31:15.2 if any of the cells remain, 31:17.2 and in fact the prion, in this case, 31:20.2 has allowed many of these cell types 31:23.0 to acquire a new invasive growth phenotype. 31:27.2 It also has create a capacity, 31:30.2 in some of the strains, 31:32.3 to flocculate, that is, to group together, 31:35.0 which really changes their growth properties. 31:36.3 It really makes them, in many ways, 31:39.0 function as a community of yeast rather than as individuals. 31:42.2 And you can see that in different strains, 31:45.0 we're getting different phenotypes. 31:47.0 So, again, a variety of different phenotypes, 31:50.0 and MOT3, by the way, 31:52.1 is a transcription repressor, 31:54.1 so when it goes into the prion state 31:56.1 it can alter the expression of lots of different genes. 32:01.2 So, this had us excited and we were very, very interested in it, 32:04.1 and a lot of other people thought it was very interesting too, 32:08.0 but there were also a lot of skeptics. 32:10.1 And with good reason, 32:12.1 because after all Saccharomyces cerevisiae 32:14.2 is the best understood organism on the planet, 32:17.2 so the question arises, 32:19.2 well, if there's so many of these things, 32:21.2 why weren't these discovered before? 32:23.3 And I have an answer to one aspect of that question, 32:29.0 because I took the Cold Spring Harbor yeast course 32:31.2 many years ago. 32:33.1 It was a wonderful, wonderful course 32:35.2 and I learned so much, 32:37.0 and it empowered my research in so many ways, 32:38.2 but there was one thing 32:40.2 that was very interesting about that course. 32:42.0 They told us that whenever we found a new phenotype, 32:44.2 a new trait in a yeast cell, 32:49.1 you should cross it back to the original 32:51.1 and then look for it to segregate two-to-two, 32:54.3 so that you had some clean system in which to investigate. 32:56.2 And things that didn't segregate two-to-two 32:59.0 were complex 33:01.0 and would be too difficult to deconstruct, 33:02.2 and you shouldn't work on them. 33:04.2 Since we've been talking about these prions, 33:07.1 I've actually had an awful lot of people come up to me and say, 33:09.2 "I had these weird traits in yeast 00:33:11.17 that were segregating that way too 00:33:13.09 and my advisor made me give it up." 33:15.2 So, I think this is kind of a wonderful story 33:17.2 where we really get into habits 33:22.0 of doing science in particular types of ways, 33:24.1 and we really need to remember 33:26.2 that it's sometimes time to break away from those. 33:29.1 Anyway, the other criticism was, 33:32.2 well, so far these things had only been found 33:35.0 in laboratory strains 33:36.3 and maybe it was just an artifact of laboratory strains, 33:38.2 so we went to the same broad group of strains 33:42.1 that we'd worked with with our Hsp90 investigations, 33:46.1 strains collected from all over the world 33:48.2 with all sorts of different properties 33:51.0 and lots of different ecological niches, 33:54.2 and we asked whether or not any of them had prions. 33:59.1 And we found that a lot of them did. 34:03.2 So, here's an example of a prion that exists in a wild strain of yeast, 34:09.1 it's a wine strain, 34:10.3 and this is the cells growing in grape must media, 34:13.3 and it turned out that we were able to create 34:16.1 isogenic cells in which we 34:18.2 caused the cells to lose the prion. 34:20.1 It didn't really make any difference, really, 34:22.1 to their growth on normal media, 34:24.1 rich media that we use in the laboratory, 34:26.0 but when you look at their ability to grow 34:28.1 in grape must medium, 34:30.0 that growth was really dependent upon the prion. 34:34.0 So the prion was creating, in this case, 34:36.0 a very valuable trait for that strain. 34:40.1 And we have found, in fact, 34:43.0 that prions very frequently create 34:45.2 interesting new traits that can be beneficial. 34:49.0 In fact, in the strains that we've looked at so far, 34:51.2 about 25% of these traits 34:54.2 allow the cells to grow under conditions 34:56.1 where they otherwise just simply could not grow. 34:58.2 So, again, because they normally appear 35:01.2 quite rarely 35:03.0 and you only lose a small percentage of the cells 35:04.3 if they're not beneficial, 35:06.1 but when they do appear they allow cells to grow 35:08.1 under conditions where they couldn't otherwise, 35:10.1 we think this is a really plausible 35:12.2 bet-hedging strategy 35:14.3 for the acquisition of genetic diversity in the organism, 35:20.3 creating lots of new phenotypes, 35:23.0 which allows it to exploit fluctuating environments 35:26.1 and grow in a variety of different situations. 35:28.3 And I just showed you one phenotype from PSI; 35:31.3 we've seen many phenotypes from many other prions as well, 35:36.1 and in fact, 35:38.1 although we haven't identified many of the prions 35:40.1 that exist in those strains, 35:41.2 the genetic behavior tells us that 35:43.2 about 236 out of the 700 wild yeast strains we looked at 35:48.1 do carry prions. 35:50.0 So, the final part of this story that I want to tell you about 35:55.0 concerns the ways in which a prion 35:58.2 can influence the dynamics of the growth 36:02.2 of these microbes in communities. 36:04.2 Now, of course, 36:07.1 almost all experimental investigations in the laboratory 36:11.2 work with organisms in pure culture, 36:15.2 so, yeast growing or bacteria growing, 36:18.0 and that's natural that we started out doing experiments that way, 36:20.2 because otherwise we have everything all mixed up 36:23.2 and it's just impossible to do 36:26.2 the kinds of experimental investigation 36:28.3 that we needed to do over the last century 36:30.2 to understand biological systems. 36:33.2 But organisms never, virtually never, 36:37.3 grow under those circumstances in the wild. 36:39.3 In the wild, they grow in mixed communities. 36:43.3 So, what I'm going to tell you about is just the first case, I think, 36:49.0 of a prion influencing the dynamics of 36:53.3 community, organization, 36:56.0 and organismal communication 36:58.0 to create more diverse and more robust communities. 37:01.3 It's the only we've really looked at so far 37:04.2 and my guess is that that's just, again, 37:06.2 the tip of the iceberg. 37:08.2 So, yeast cells have 37:13.0 a very particular type of metabolism. 37:15.2 They're very, very fastidious, 37:17.1 and that's the reason why we love them. 37:19.1 When we give them glucose, 37:21.1 sugars like from grapes, 37:23.0 they take those sugars 37:25.3 and they convert them into ethanol extremely efficiently. 37:27.1 They don't use the ethanol. 37:29.1 They only make the sugars into ethanol; 37:31.3 only when all the sugar is exhausted 37:34.1 do they start turning around and using the ethanol. 37:36.2 So they will not grow on another carbon source 37:42.1 if there's any glucose present -- 37:45.1 very, very fastidious. 37:49.1 But we found a prion 37:52.2 that cells can acquire 37:54.2 that allows them to bypass this system. 37:55.3 They don't grow quite as well in pure glucose 38:00.2 when they have that prion, 38:01.2 and that's probably why this was 38:04.0 not really found and looked at before, 38:06.0 but in mixed carbon sources 38:08.2 where there's other carbon sources around, 38:10.2 and a little bit of glucose too, 38:12.2 those cells can now grow when they otherwise 38:15.1 would not be able to. 38:17.2 So, how did we investigate this story? 38:20.3 Well, we took advantage of a glucose mimetic. 38:24.1 It's a compound called glucosamine, 38:27.2 that looks just like glucose, probably to most of you, 38:31.1 and it does to the yeast cells 38:32.3 -- it has this little amino group here -- 38:35.0 but what it does is the yeast cells 38:37.3 take this glucosamine up, they think they're in glucose, 38:39.2 and the shut off the pathways for utilization 38:42.2 of all other carbon sources. 38:45.2 And so here you see an example of this. 38:48.0 We've got cells growing without the prion. 38:52.2 Growing in pure glycerol they're just fine, 38:55.3 but if you try to get them to grow in glycerol 38:57.3 with just a little bit of this glucosamine in there, 39:00.1 they think, 39:01.3 "Oh no, I do not want to use any other carbon source, 00:39:03.14 I only want to use glucose, 00:39:05.13 and I won't use that other carbon source 00:39:07.11 until all the glucose is gone," 39:09.0 they can't metabolize it, so they're just stuck. 39:12.2 This allowed us to search for strains 39:15.2 that might bypass that metabolic problem, 39:22.1 and allow the cells to grow in the presence 39:25.1 of other carbon sources 39:27.0 when there was a little bit of glucose present. 39:28.3 And as you can see, we've got strains here 39:31.1 that do that very, very well. 39:32.2 This strain is genetically identical to this strain 39:36.1 and we've tested that in many, many ways. 39:38.2 Moreover, these cells, 39:41.1 once they acquire that ability to grow on that other carbon source, 39:44.2 can be processed and passaged 39:47.0 for hundreds of generation, 39:49.1 back on pure glucose, 39:51.2 but they remember. 39:53.0 They retain that trait, 39:54.2 it's a biological memory, 39:57.1 and they retain that trait for many, many generations. 40:01.2 That allows them, next time they're put on 40:04.0 that diverse carbon source, 40:06.1 to be able to grow on them. 40:08.1 So, it really changes their metabolic potential 40:11.2 in a really quite strong way. 40:14.2 Now, the cool thing about this 40:17.1 in terms of functioning in a community... 40:19.1 when you think about it, 40:21.1 carbon source utilization strategies 40:23.0 might change quite a bit 40:25.1 depending on whether you have other neighbors that are competing with you 40:27.2 for those carbon sources. 40:30.1 And this is something we found actually by accident. 40:33.2 Jessica Brown was working on strains, 40:37.0 looking at what causes prion switching, 40:39.2 and she suddenly found a colony 40:42.2 around which there were all kinds of strains 40:44.2 that had switched into the prion state, 40:46.1 and that colony in the middle was a bacteria. 40:48.2 So, in this experiment we tested that more rigorously. 40:52.2 Do bacteria have the capacity to secrete a factor 40:55.2 that will cause the yeast cells 40:57.2 to switch their metabolism in a heritable way 41:00.1 by the induction of this prion? 41:02.0 So, in this experiment, what we've done 41:04.0 is we've plated... 41:06.0 left these wells empty 41:07.3 and we've plated the gar- yeast cells here, 41:11.0 the GAR+ yeast cells 41:13.1 -- GAR is for glucose-associated repression, 41:14.2 I should have said that, that's the name of that prion -- 41:17.0 and then we've plated gar- cells here. 41:20.3 So, these cells, remember, are genetically identical; 41:23.3 the only difference between them is that 41:26.1 these cells have switched into a prion state 41:28.2 that is inherited in a non-Mendelian fashion, 41:33.2 because it's based upon protein-based inheritance. 41:36.1 And the cells are being plated on glycerol 41:39.0 with, again, just a little bit of glucosamine. 41:42.1 And you can see, by the way, 41:43.2 that the cells do switch spontaneously, 41:45.0 every once in a while, 41:46.3 into this prion state, 41:48.1 which allows them to grow, 41:49.2 but they really only switch that way every once in a while. 41:52.0 This is a dilution of strains across this plate. 41:57.1 Now, the next experiment is done exactly the same way, 42:00.0 except we're going to plate bacteria 42:03.1 that seem to have the capacity to induce this prion here. 42:06.1 And what you can see is that 42:09.0 the presence of the bacteria on that plate... 42:11.1 a substance has diffused down the plate 42:15.1 and influenced these yeast cells here 42:18.1 to now switch their metabolic state 42:20.3 and be able to grow under this condition 42:22.1 when they otherwise wouldnÃ¢â‚¬â„¢t, 42:24.1 but it's a diffusible compound 42:26.2 and it doesn't get far enough 42:28.1 to get all the way down to this row, here. 42:30.0 We established by lots of experiments 42:33.0 that I won't take the time to show you, 42:34.2 but we could use conditioned media that bacteria had grown in, 42:39.1 and get rid of the bacteria, 42:40.2 and we could use the conditioned media 42:43.1 to induce this heritable trait. 42:46.2 So, what happens? 42:48.2 The cells usually are using glucose 42:50.2 and they make lots of ethanol and only some ATP, 42:53.1 but when they're in the presence of glucose 42:55.1 and other carbon sources, 42:57.0 this prion now allows them 42:59.0 to make less ethanol, 43:01.3 but lots of ATP, 43:04.2 and what does that do? 43:06.1 It causes both organisms to flourish. 43:10.1 So, it turns out that when the yeast cells switch, 43:15.1 as I mentioned, 43:16.2 it allows them to grow on different kinds of carbon sources, 43:18.1 even when glucose is present. 43:19.2 It also, for reason we don't quite understand, 43:22.2 creats increased longevity in those cells 43:25.1 #NAME? 43:27.3 for much longer periods of time -- 43:29.1 and the bacteria get a big advantage out of it, 43:32.1 because the yeast cells are making less ethanol 43:36.2 and that means that the bacteria 43:39.2 can grow up to much higher concentrations, 43:41.1 because the bacteria don't like ethanol 43:43.0 #NAME? 43:45.0 Now, it turns out that this is 43:47.3 a very highly conserved property of yeast and bacterial strains 43:50.3 from all over the world. 43:52.2 We've found lots and lots of yeast strains 43:57.1 are capable of this kind of a switch, 43:59.0 and many, many different bacteria 44:01.1 are capable of inducing it. 44:03.1 But does it matter in the real world? 44:05.1 Well, I can tell you one way in which it clearly matters in the real world, 44:08.1 and that is it spoils wine fermentations. 44:12.2 So, the yeast are making less ethanol, 44:15.0 they're making other kinds of metabolic byproducts, 44:17.1 and it really makes lousy wine, 44:19.1 and in fact it turns out the bacteria 44:22.2 that contaminate spoiled wine preparations, 44:25.3 that winologists have been studying 44:28.1 for a long period of time, 44:30.0 turn out to be super-inducers. 44:33.0 They make lots of this compound that switches the yeast cells 44:36.1 into this new prion state. 44:38.2 So, the yeast get tremendous advantages out of it, 44:42.1 the bacteria get tremendous advantages out of it, 44:44.1 it's to the detriment of man, 44:46.3 but to the benefit of both those organisms. 44:48.3 And again, in terms of the conservation of this, 44:51.2 across tens of millions of years of yeast evolution, 44:55.2 these strains have really exactly... 44:58.2 what seems to be exactly the same mechanisms. 45:01.2 Bruxellensis, which has diverged from Saccharomyces... 45:05.3 these strains don't have it, 45:07.1 they have a different kind of metabolic profile... 45:09.2 bruxellensis is another yeast 45:13.1 that has that same kind of fastidious lifestyle 45:15.0 in terms of carbon utilization 45:17.1 that Saccharomyces has, 45:18.2 and it also has that GAR prion-like state. 45:20.3 Even pombe, which diverged hundreds of millions of years ago, 45:25.1 although the mechanism isn't quite the same, 45:27.2 has the capacity to switch into an epigenetic state 45:31.2 that changes its metabolism heritably, 45:34.0 so vast amounts of evolutionary distances... 45:36.1 these abilities to switch carbon source in a heritable way, 45:40.3 induced by environmental factors, 45:42.2 has been conserved. 45:44.0 So, here we have really 45:46.0 what I consider the ultimate example of Lamarckian evolution. 45:49.1 We have chemical communication 45:52.1 between a prokaryote and a eukaryote 45:54.2 that transforms metabolism in a heritable way, 45:58.1 and the simple exposure of the yeast cells 46:01.1 to that chemical compound 46:03.2 causes them to change their biology 46:07.1 in a way that's heritable 46:09.1 for hundreds of generations. 46:14.1 So, again, 46:17.0 when considering Jean-Baptiste Lamarck, 46:19.2 I think it's time to give him back his dignity. 46:22.2 There are mechanisms by which changes in the environment 46:25.2 can produce new traits 46:28.1 that can be passed on to progeny. 46:31.0 So, once again, 46:32.3 I want to end my lecture 46:34.2 by acknowledging the amazing and wonderful people 46:36.2 in my laboratory 46:37.3 who have done this work over the years. 46:40.1 I've spoken about things that each of them have done 46:44.0 and if you're interested in this work, 46:46.1 I would really urge you to take a look at some of the papers. 46:49.1 I think they're pretty cool. 46:52.0 Thank you.

Talk Overview

In Part 1, Dr. Lindquist explains the problem of protein folding. Proteins leave the ribosome as long, linear chains of amino acids but they need to fold into complex three dimensional shapes in the extremely crowded environment of the cytoplasm. Since protein misfolding can be disastrous for cells, proteins known as heat shock proteins (HSPs) have evolved to facilitate proper protein folding. Lindquist explains that sometimes the heat shock response becomes unbalanced resulting in human disease. In the case of cancer, HSPs help cancer cells survive many stresses that would typically kill them. In contrast, many neurodegenerative diseases are a result of protein misfolding and aggregation suggesting that, in these diseases, HSPs are not activated when they should be.

Yeast have many of the same cellular processes as humans including a stress response to aid in protein folding and prevent protein aggregation. In Part 2, Lindquist describes how genetic screens in yeast helped scientists identify mutations that increased the formation of aggregates similar to those found in neurodegenerative diseases. Furthermore a screen in yeast of ~500,000 chemicals identified a number of compounds that prevented protein aggregation. Results from both experiments have since been validated in mice and human neuronal models.

When cells undergo stress, the expression of HSPs increases. In Part 3, Lindquist explains that while most HSPs are expressed only as needed, Hsp90 is expressed in excess. This “buffer” of Hsp90 facilitates the folding of some mutant proteins (such as v-src) that would usually misfold and be degraded by the cell. Thus, Hsp90 potentiates the impact of these mutations. Interestingly, the Hsp90 “buffer” can also act to hide or suppress the impact of other mutations. These “hidden” mutations are found when cells are stressed reducing the pool of available Hsp90. Thus, Hsp90 provides a plausible mechanism for allowing genetic diversity and fluctuating environments to fuel the pace of evolutionary change.

In her last talk, Lindquist focuses on prion proteins. Prions are perhaps best known as the infectious agents in diseases such as mad cow disease. However, Lindquist argues that there are many great things about prions too. They provide a protein-based mechanism of inheritance that allows organisms to develop new traits, quickly and reversibly, and thereby adapt to new environments. Working in yeast, Lindquist and her colleagues were able to identify numerous prion-like proteins that are induced at different levels, depending on the temperature, pH or presence of bacteria. Expression of prions caused heritable, phenotypic changes in the yeast demonstrating that prions are another mechanism by which environmental changes can induce new traits that can be passed onto progeny.

Speaker Bio

Susan Lindquist was a member and former Director of the Whitehead Institute for Biomedical Research. She was also a Howard Hughes Medical Institute Investigator and Professor of Biology at the Massachusetts Institute of Technology. She received her Ph.D. in biology from Harvard and was a postdoctoral fellow of the American Cancer Society. Lindquist was on… Continue Reading

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This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. MCB-1052331.

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